Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing...

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Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010 Jing Li (UofO) MAT 1332 C March 8, 2010 1 / 39

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Page 1: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Calculus for Life SciencesMAT 1332 C Winter 2010

Jing Li

Department of Mathematics and StatisticsUniversity of Ottawa

March 8, 2010

Jing Li (UofO) MAT 1332 C March 8, 2010 1 / 39

Page 2: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Outline

1 Midterm 1 Grade Statistics

2 Feedback Questionaire

3 Linear Algebra I: Solving Linear Systems of Equations

4 Linear Algebra II: Vectors and MatricesDefinitionOperations

Basic Matrix OperationsMatrix-Vector MultiplicationMatrix-Matrix Multiplication

Jing Li (UofO) MAT 1332 C March 8, 2010 2 / 39

Page 3: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Midterm 1 Grade Statistics

Table: Midterm 1 Grade Statistics

Count: 169Average: 18.8Median: 19.5Maximum: 29.5Minimun: 2.0Standard Deviation: 5.91

Jing Li (UofO) MAT 1332 C March 8, 2010 3 / 39

Page 4: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Midterm 1 Grade Statistics

Midterm 1 Grade Statistics

Jing Li (UofO) MAT 1332 C March 8, 2010 4 / 39

Page 5: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Feedback Questionaire

Aspects that help learning

Algorithms

DGDs

Help available outside class

Examples in class

Assignments

Math help center

Review sessions

Jing Li (UofO) MAT 1332 C March 8, 2010 5 / 39

Page 6: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Feedback Questionaire

Suggestions:

Better English

Better classroom

Better blackboard presentation (notes )

Slow down (especially in the end of class)

Practice midterms

Better control of the class (too noisy)

Better organization and better introduction

More explanation

Application in real lives

DGDs should focus on similar problems for midterms and assignments.

get class more involved in the lecture.

More examples (difficult ones, close to tests and assignments, less parameters)

Better TA picking

do assignment questions in class

more office hours

Jing Li (UofO) MAT 1332 C March 8, 2010 6 / 39

Page 7: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Feedback Questionaire

Changes that can be implementedget help from me, TA, help center.NOTEs (online)Better lecture presentation (organization, introduction, examples,explanation, application)Slow down speedBetter control of the class (quite, get class more involved inlectures)Better questions in DGDs. (do hard assignment questions inDGDs)

Jing Li (UofO) MAT 1332 C March 8, 2010 7 / 39

Page 8: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Reduced row-echelon form

Definition

leading entry, row-echelon form, and reduced row-echelon form

The leading entry of a row in a matrix is the leftmost nonzero coefficient in thatrow.

A matrix is in row-echelon form if the following three rules are true

(1) Rows of zeros are blew any nonzero row.(2) The leading entry of any row is to the right of any leading entry in

any row above it.(3) All entires in the column below a leading entry are zero.

A matrix is in reduced row-echelon form if it is in row-echelon form and inaddition

(4) Each leading entry is 1.(5) All entries in the column above a leading entry are zero.

Jing Li (UofO) MAT 1332 C March 8, 2010 8 / 39

Page 9: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Reduced row-echelon form

Definition

leading entry, row-echelon form, and reduced row-echelon form

The leading entry of a row in a matrix is the leftmost nonzero coefficient in thatrow.

A matrix is in row-echelon form if the following three rules are true

(1) Rows of zeros are blew any nonzero row.(2) The leading entry of any row is to the right of any leading entry in

any row above it.(3) All entires in the column below a leading entry are zero.

A matrix is in reduced row-echelon form if it is in row-echelon form and inaddition

(4) Each leading entry is 1.(5) All entries in the column above a leading entry are zero.

Jing Li (UofO) MAT 1332 C March 8, 2010 8 / 39

Page 10: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Reduced row-echelon form

Definition

leading entry, row-echelon form, and reduced row-echelon form

The leading entry of a row in a matrix is the leftmost nonzero coefficient in thatrow.

A matrix is in row-echelon form if the following three rules are true

(1) Rows of zeros are blew any nonzero row.

(2) The leading entry of any row is to the right of any leading entry inany row above it.

(3) All entires in the column below a leading entry are zero.

A matrix is in reduced row-echelon form if it is in row-echelon form and inaddition

(4) Each leading entry is 1.(5) All entries in the column above a leading entry are zero.

Jing Li (UofO) MAT 1332 C March 8, 2010 8 / 39

Page 11: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Reduced row-echelon form

Definition

leading entry, row-echelon form, and reduced row-echelon form

The leading entry of a row in a matrix is the leftmost nonzero coefficient in thatrow.

A matrix is in row-echelon form if the following three rules are true

(1) Rows of zeros are blew any nonzero row.(2) The leading entry of any row is to the right of any leading entry in

any row above it.

(3) All entires in the column below a leading entry are zero.

A matrix is in reduced row-echelon form if it is in row-echelon form and inaddition

(4) Each leading entry is 1.(5) All entries in the column above a leading entry are zero.

Jing Li (UofO) MAT 1332 C March 8, 2010 8 / 39

Page 12: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Reduced row-echelon form

Definition

leading entry, row-echelon form, and reduced row-echelon form

The leading entry of a row in a matrix is the leftmost nonzero coefficient in thatrow.

A matrix is in row-echelon form if the following three rules are true

(1) Rows of zeros are blew any nonzero row.(2) The leading entry of any row is to the right of any leading entry in

any row above it.(3) All entires in the column below a leading entry are zero.

A matrix is in reduced row-echelon form if it is in row-echelon form and inaddition

(4) Each leading entry is 1.(5) All entries in the column above a leading entry are zero.

Jing Li (UofO) MAT 1332 C March 8, 2010 8 / 39

Page 13: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Reduced row-echelon form

Definition

leading entry, row-echelon form, and reduced row-echelon form

The leading entry of a row in a matrix is the leftmost nonzero coefficient in thatrow.

A matrix is in row-echelon form if the following three rules are true

(1) Rows of zeros are blew any nonzero row.(2) The leading entry of any row is to the right of any leading entry in

any row above it.(3) All entires in the column below a leading entry are zero.

A matrix is in reduced row-echelon form if it is in row-echelon form and inaddition

(4) Each leading entry is 1.(5) All entries in the column above a leading entry are zero.

Jing Li (UofO) MAT 1332 C March 8, 2010 8 / 39

Page 14: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Reduced row-echelon form

Definition

leading entry, row-echelon form, and reduced row-echelon form

The leading entry of a row in a matrix is the leftmost nonzero coefficient in thatrow.

A matrix is in row-echelon form if the following three rules are true

(1) Rows of zeros are blew any nonzero row.(2) The leading entry of any row is to the right of any leading entry in

any row above it.(3) All entires in the column below a leading entry are zero.

A matrix is in reduced row-echelon form if it is in row-echelon form and inaddition

(4) Each leading entry is 1.

(5) All entries in the column above a leading entry are zero.

Jing Li (UofO) MAT 1332 C March 8, 2010 8 / 39

Page 15: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Reduced row-echelon form

Definition

leading entry, row-echelon form, and reduced row-echelon form

The leading entry of a row in a matrix is the leftmost nonzero coefficient in thatrow.

A matrix is in row-echelon form if the following three rules are true

(1) Rows of zeros are blew any nonzero row.(2) The leading entry of any row is to the right of any leading entry in

any row above it.(3) All entires in the column below a leading entry are zero.

A matrix is in reduced row-echelon form if it is in row-echelon form and inaddition

(4) Each leading entry is 1.(5) All entries in the column above a leading entry are zero.

Jing Li (UofO) MAT 1332 C March 8, 2010 8 / 39

Page 16: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 5.

Categorize the following systems according to the above definition:241 2 00 3 10 0 2

35 ,

242 0 2 40 1 1 00 0 0 7

35 ,

241 0 20 1 10 0 0

35 ,

241 0 2 00 1 −3 00 0 0 1

35 ,

242 0 21 1 10 3 0

35Solution:

row-echelon form but not reduced: (1), (2)

reduced row-echelon form: (3), (4)

neither row-echelon nor reduced row-echelon form: (5)

Jing Li (UofO) MAT 1332 C March 8, 2010 9 / 39

Page 17: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 5.

Categorize the following systems according to the above definition:241 2 00 3 10 0 2

35 ,

242 0 2 40 1 1 00 0 0 7

35 ,

241 0 20 1 10 0 0

35 ,

241 0 2 00 1 −3 00 0 0 1

35 ,

242 0 21 1 10 3 0

35Solution:

row-echelon form but not reduced: (1), (2)

reduced row-echelon form: (3), (4)

neither row-echelon nor reduced row-echelon form: (5)

Jing Li (UofO) MAT 1332 C March 8, 2010 9 / 39

Page 18: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 5.

Categorize the following systems according to the above definition:241 2 00 3 10 0 2

35 ,

242 0 2 40 1 1 00 0 0 7

35 ,

241 0 20 1 10 0 0

35 ,

241 0 2 00 1 −3 00 0 0 1

35 ,

242 0 21 1 10 3 0

35Solution:

row-echelon form but not reduced: (1), (2)

reduced row-echelon form: (3), (4)

neither row-echelon nor reduced row-echelon form: (5)

Jing Li (UofO) MAT 1332 C March 8, 2010 9 / 39

Page 19: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35

−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 20: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 21: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 5

0 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 22: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50

− 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 23: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2

− 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 24: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 |

− 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 25: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35

R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 26: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→

24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 27: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −10

0 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 28: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100

0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 29: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0

5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 30: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 |

20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 31: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35

(−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 32: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 33: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 5

0 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 34: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50

2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 35: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2

3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 36: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 |

100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 37: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 10

0 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 38: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100

0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 39: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0

1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 40: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 |

4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 41: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35

−R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 42: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→

24 1 1 0 | 10 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 43: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24

1 1 0 | 10 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 44: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1

1 0 | 10 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 45: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1

0 | 10 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 46: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 |

10 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 47: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 48: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0

2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 49: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2

0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 50: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 |

− 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 51: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35

12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 52: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 1

0 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 53: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10

1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 54: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1

0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 55: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 |

− 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 56: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35

−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 57: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 58: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24

1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 59: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1

0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 60: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0

0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 61: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 |

20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 62: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35

Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 63: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 6: Exactly One Solution

8<:x1 + x2 + x3 = 52x1 − x3 = 0

x2 + 4x3 = 15

Solution: Using the matrix notation,24 1 1 1 | 52 0 −1 | 00 1 4 | 15

35−2R1 + R2−−−−−−−→

24 1 1 1 | 50 − 2 − 3 | − 100 1 4 | 15

35R2 + 2R3−−−−−−→24 1 1 1 | 50 −2 −3 | −100 0 5 | 20

35 (−1)R2;15 R3

−−−−−−−→

24 1 1 1 | 50 2 3 | 100 0 1 | 4

35 −R3 + R1;−3R3 + R2−−−−−−−−−→24 1 1 0 | 1

0 2 0 | − 20 0 1 | 4

35 12

R2

−−→

24 1 1 0 | 10 1 0 | − 10 0 1 | 4

35−R2 + R1−−−−−−→

24 1 0 0 | 20 1 0 | −10 0 1 | 4

35Hence, we have solution x1 = 2, x2 = −1, x3 = 4 or (2,−1, 4).

Jing Li (UofO) MAT 1332 C March 8, 2010 10 / 39

Page 64: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35

−2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 65: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 66: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 3

0 − 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 67: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30

− 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 68: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2

1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 69: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 |

− 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 70: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 4

0 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 71: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40

− 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 72: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2

1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 73: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 |

− 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 74: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 | − 2

35

−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 75: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→

24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 76: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −4

0 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 77: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40

0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 78: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0

0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 79: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 |

2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 80: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35

The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 81: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 82: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 83: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 84: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 7: No Solution

8<:2x1 − x2 + x3 = 34x1 − 4x2 + 3x3 = 22x1 − 3x2 + 2x3 = 1

Solution:24 2 −1 1 | 34 −4 3 | 22 −3 2 | 1

35 −2R1 + R2

−R1 + R3−−−−−−−−−→

24 2 −1 1 | 30 − 2 1 | − 40 − 2 1 | − 2

35−R2 + R3−−−−−−→24 2 −1 1 | 30 −2 1 | −40 0 0 | 2

35The last equation: 0 · x1 + 0 · x2 + 0 · x3 = 2, is wrong statement.

This is clearly impossible.

This means that this system does not have a solution.

Hence, this system is inconsistent.

Jing Li (UofO) MAT 1332 C March 8, 2010 11 / 39

Page 85: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35

−R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 86: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 87: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 4

0 1 2 | 20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 88: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40

1 2 | 20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 89: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1

2 | 20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 90: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 |

20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 91: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 2

0 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 92: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20

0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 93: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0

0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 94: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0 0 |

0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 95: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0 0 | 0

35

The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 96: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0 0 | 0

35

The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 97: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 98: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement,

and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 99: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 100: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 101: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t ,

then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 102: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 103: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 104: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 8: Infinitely Many Solutions

8<:x1 − 3x2 + x3 = 4x1 − 2x2 + 3x3 = 62x1 − 6x2 + 2x3 = 8

Solution:24 1 −3 1 | 41 −2 3 | 62 −6 2 | 8

35 −R1 + R2

−2R1 + R3−−−−−−−−−→

24 1 −3 1 | 40 1 2 | 20 0 0 | 0

35The last row reads: 0 · x1 + 0 · x2 + 0 · x3 = 0.

This equation is a correct statement, and is true for all values of x1, x2, x3.

The second equation is x2 + 2x3 = 2.

We introduce a free variable by denoting x3 = t , then x2 = 2− 2x3 = 2− 2t .

The first equation: x1 − 3x2 + x3 = 4, =⇒x1 = 4 + 3x2 − x3 = 4 + 3(2− 2t)− t = 10− 7t .

Hence, the solution set is {(x1, x2, x3) : x1 = 10− 7t , x2 = 2− 2t , x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 12 / 39

Page 105: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 9: Underdetermined System: A System has fewer equations than variables

2x1 + 2x2 − x3 = 12x1 − x2 + x3 = 2

Solution: »2 2 −1 | 12 −1 1 | 2

−R1 + R2−−−−−−→

»2 2 −1 | 10 − 3 2 | 1

–This time, the last row reads −3x2 + 2x3 = 1.Whatever we choose for x3 we can always find a x2 to make the equation true.Hence this system has infinitely many solutions.We set x3 = t as a free variable, then we get x2 = 2

3 x3 − 13 = 2

3 t − 13 .

We plug these back to the first equation to solve for x1:

x1 = −x2 +12

x3 +12

= −(23

t − 13

) +12

t +12

= − t6

+56

.

Hence, the solution set is

{(x1, x2, x3) : x1 = − t6

+56

, x2 =23

t − 13

, x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 13 / 39

Page 106: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 9: Underdetermined System: A System has fewer equations than variables

2x1 + 2x2 − x3 = 12x1 − x2 + x3 = 2

Solution: »2 2 −1 | 12 −1 1 | 2

–−R1 + R2−−−−−−→

»2 2 −1 | 10 − 3 2 | 1

–This time, the last row reads −3x2 + 2x3 = 1.Whatever we choose for x3 we can always find a x2 to make the equation true.Hence this system has infinitely many solutions.We set x3 = t as a free variable, then we get x2 = 2

3 x3 − 13 = 2

3 t − 13 .

We plug these back to the first equation to solve for x1:

x1 = −x2 +12

x3 +12

= −(23

t − 13

) +12

t +12

= − t6

+56

.

Hence, the solution set is

{(x1, x2, x3) : x1 = − t6

+56

, x2 =23

t − 13

, x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 13 / 39

Page 107: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 9: Underdetermined System: A System has fewer equations than variables

2x1 + 2x2 − x3 = 12x1 − x2 + x3 = 2

Solution: »2 2 −1 | 12 −1 1 | 2

–−R1 + R2−−−−−−→

»2 2 −1 | 1

0 − 3 2 | 1

–This time, the last row reads −3x2 + 2x3 = 1.Whatever we choose for x3 we can always find a x2 to make the equation true.Hence this system has infinitely many solutions.We set x3 = t as a free variable, then we get x2 = 2

3 x3 − 13 = 2

3 t − 13 .

We plug these back to the first equation to solve for x1:

x1 = −x2 +12

x3 +12

= −(23

t − 13

) +12

t +12

= − t6

+56

.

Hence, the solution set is

{(x1, x2, x3) : x1 = − t6

+56

, x2 =23

t − 13

, x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 13 / 39

Page 108: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 9: Underdetermined System: A System has fewer equations than variables

2x1 + 2x2 − x3 = 12x1 − x2 + x3 = 2

Solution: »2 2 −1 | 12 −1 1 | 2

–−R1 + R2−−−−−−→

»2 2 −1 | 10

− 3 2 | 1

–This time, the last row reads −3x2 + 2x3 = 1.Whatever we choose for x3 we can always find a x2 to make the equation true.Hence this system has infinitely many solutions.We set x3 = t as a free variable, then we get x2 = 2

3 x3 − 13 = 2

3 t − 13 .

We plug these back to the first equation to solve for x1:

x1 = −x2 +12

x3 +12

= −(23

t − 13

) +12

t +12

= − t6

+56

.

Hence, the solution set is

{(x1, x2, x3) : x1 = − t6

+56

, x2 =23

t − 13

, x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 13 / 39

Page 109: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 9: Underdetermined System: A System has fewer equations than variables

2x1 + 2x2 − x3 = 12x1 − x2 + x3 = 2

Solution: »2 2 −1 | 12 −1 1 | 2

–−R1 + R2−−−−−−→

»2 2 −1 | 10 − 3

2 | 1

–This time, the last row reads −3x2 + 2x3 = 1.Whatever we choose for x3 we can always find a x2 to make the equation true.Hence this system has infinitely many solutions.We set x3 = t as a free variable, then we get x2 = 2

3 x3 − 13 = 2

3 t − 13 .

We plug these back to the first equation to solve for x1:

x1 = −x2 +12

x3 +12

= −(23

t − 13

) +12

t +12

= − t6

+56

.

Hence, the solution set is

{(x1, x2, x3) : x1 = − t6

+56

, x2 =23

t − 13

, x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 13 / 39

Page 110: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 9: Underdetermined System: A System has fewer equations than variables

2x1 + 2x2 − x3 = 12x1 − x2 + x3 = 2

Solution: »2 2 −1 | 12 −1 1 | 2

–−R1 + R2−−−−−−→

»2 2 −1 | 10 − 3 2 |

1

–This time, the last row reads −3x2 + 2x3 = 1.Whatever we choose for x3 we can always find a x2 to make the equation true.Hence this system has infinitely many solutions.We set x3 = t as a free variable, then we get x2 = 2

3 x3 − 13 = 2

3 t − 13 .

We plug these back to the first equation to solve for x1:

x1 = −x2 +12

x3 +12

= −(23

t − 13

) +12

t +12

= − t6

+56

.

Hence, the solution set is

{(x1, x2, x3) : x1 = − t6

+56

, x2 =23

t − 13

, x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 13 / 39

Page 111: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 9: Underdetermined System: A System has fewer equations than variables

2x1 + 2x2 − x3 = 12x1 − x2 + x3 = 2

Solution: »2 2 −1 | 12 −1 1 | 2

–−R1 + R2−−−−−−→

»2 2 −1 | 10 − 3 2 | 1

This time, the last row reads −3x2 + 2x3 = 1.Whatever we choose for x3 we can always find a x2 to make the equation true.Hence this system has infinitely many solutions.We set x3 = t as a free variable, then we get x2 = 2

3 x3 − 13 = 2

3 t − 13 .

We plug these back to the first equation to solve for x1:

x1 = −x2 +12

x3 +12

= −(23

t − 13

) +12

t +12

= − t6

+56

.

Hence, the solution set is

{(x1, x2, x3) : x1 = − t6

+56

, x2 =23

t − 13

, x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 13 / 39

Page 112: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 9: Underdetermined System: A System has fewer equations than variables

2x1 + 2x2 − x3 = 12x1 − x2 + x3 = 2

Solution: »2 2 −1 | 12 −1 1 | 2

–−R1 + R2−−−−−−→

»2 2 −1 | 10 − 3 2 | 1

–This time, the last row reads −3x2 + 2x3 = 1.

Whatever we choose for x3 we can always find a x2 to make the equation true.Hence this system has infinitely many solutions.We set x3 = t as a free variable, then we get x2 = 2

3 x3 − 13 = 2

3 t − 13 .

We plug these back to the first equation to solve for x1:

x1 = −x2 +12

x3 +12

= −(23

t − 13

) +12

t +12

= − t6

+56

.

Hence, the solution set is

{(x1, x2, x3) : x1 = − t6

+56

, x2 =23

t − 13

, x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 13 / 39

Page 113: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 9: Underdetermined System: A System has fewer equations than variables

2x1 + 2x2 − x3 = 12x1 − x2 + x3 = 2

Solution: »2 2 −1 | 12 −1 1 | 2

–−R1 + R2−−−−−−→

»2 2 −1 | 10 − 3 2 | 1

–This time, the last row reads −3x2 + 2x3 = 1.Whatever we choose for x3 we can always find a x2 to make the equation true.

Hence this system has infinitely many solutions.We set x3 = t as a free variable, then we get x2 = 2

3 x3 − 13 = 2

3 t − 13 .

We plug these back to the first equation to solve for x1:

x1 = −x2 +12

x3 +12

= −(23

t − 13

) +12

t +12

= − t6

+56

.

Hence, the solution set is

{(x1, x2, x3) : x1 = − t6

+56

, x2 =23

t − 13

, x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 13 / 39

Page 114: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 9: Underdetermined System: A System has fewer equations than variables

2x1 + 2x2 − x3 = 12x1 − x2 + x3 = 2

Solution: »2 2 −1 | 12 −1 1 | 2

–−R1 + R2−−−−−−→

»2 2 −1 | 10 − 3 2 | 1

–This time, the last row reads −3x2 + 2x3 = 1.Whatever we choose for x3 we can always find a x2 to make the equation true.Hence this system has infinitely many solutions.

We set x3 = t as a free variable, then we get x2 = 23 x3 − 1

3 = 23 t − 1

3 .

We plug these back to the first equation to solve for x1:

x1 = −x2 +12

x3 +12

= −(23

t − 13

) +12

t +12

= − t6

+56

.

Hence, the solution set is

{(x1, x2, x3) : x1 = − t6

+56

, x2 =23

t − 13

, x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 13 / 39

Page 115: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 9: Underdetermined System: A System has fewer equations than variables

2x1 + 2x2 − x3 = 12x1 − x2 + x3 = 2

Solution: »2 2 −1 | 12 −1 1 | 2

–−R1 + R2−−−−−−→

»2 2 −1 | 10 − 3 2 | 1

–This time, the last row reads −3x2 + 2x3 = 1.Whatever we choose for x3 we can always find a x2 to make the equation true.Hence this system has infinitely many solutions.We set x3 = t as a free variable, then we get x2 = 2

3 x3 − 13 = 2

3 t − 13 .

We plug these back to the first equation to solve for x1:

x1 = −x2 +12

x3 +12

= −(23

t − 13

) +12

t +12

= − t6

+56

.

Hence, the solution set is

{(x1, x2, x3) : x1 = − t6

+56

, x2 =23

t − 13

, x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 13 / 39

Page 116: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 9: Underdetermined System: A System has fewer equations than variables

2x1 + 2x2 − x3 = 12x1 − x2 + x3 = 2

Solution: »2 2 −1 | 12 −1 1 | 2

–−R1 + R2−−−−−−→

»2 2 −1 | 10 − 3 2 | 1

–This time, the last row reads −3x2 + 2x3 = 1.Whatever we choose for x3 we can always find a x2 to make the equation true.Hence this system has infinitely many solutions.We set x3 = t as a free variable, then we get x2 = 2

3 x3 − 13 = 2

3 t − 13 .

We plug these back to the first equation to solve for x1:

x1 = −x2 +12

x3 +12

= −(23

t − 13

) +12

t +12

= − t6

+56

.

Hence, the solution set is

{(x1, x2, x3) : x1 = − t6

+56

, x2 =23

t − 13

, x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 13 / 39

Page 117: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 9: Underdetermined System: A System has fewer equations than variables

2x1 + 2x2 − x3 = 12x1 − x2 + x3 = 2

Solution: »2 2 −1 | 12 −1 1 | 2

–−R1 + R2−−−−−−→

»2 2 −1 | 10 − 3 2 | 1

–This time, the last row reads −3x2 + 2x3 = 1.Whatever we choose for x3 we can always find a x2 to make the equation true.Hence this system has infinitely many solutions.We set x3 = t as a free variable, then we get x2 = 2

3 x3 − 13 = 2

3 t − 13 .

We plug these back to the first equation to solve for x1:

x1 = −x2 +12

x3 +12

= −(23

t − 13

) +12

t +12

= − t6

+56

.

Hence, the solution set is

{(x1, x2, x3) : x1 = − t6

+56

, x2 =23

t − 13

, x3 = t , t ∈ R}

Jing Li (UofO) MAT 1332 C March 8, 2010 13 / 39

Page 118: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35

−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 119: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 120: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 2

0 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 121: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20

− 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 122: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 |

1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 123: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35

−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 124: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 125: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 2

0 1 | − 12

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 126: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20

1 | − 12

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 127: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 |

− 12

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 128: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35

R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 129: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 130: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24

2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 131: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2

0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 132: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 |

12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 133: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 134: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1

0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 135: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 |

52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 136: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35

12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 137: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 138: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24

1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 139: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1

0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 140: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 |

14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 141: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35

x1 = 14 from the first row, x1 = 5

2 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 142: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 143: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 144: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 10: Overdetermined System: a system has more equations than variables

8<:2x1 − x2 = 1x1 + x2 = 2x1 − x2 = 3

Solution:24 2 −1 | 11 1 | 21 −1 | 3

35−R2 + R3−−−−−−→

24 2 −1 | 11 1 | 20 − 2 | 1

35−12

R3

−−−−→

24 2 −1 | 11 1 | 20 1 | − 1

2

35R3 + R1

−R3 + R2−−−−−−−−→

24 2 0 | 12

1 0 | 52

0 1 | − 12

35 12

R1

−−→

24 1 0 | 14

1 0 | 52

0 1 | − 12

35x1 = 1

4 from the first row, x1 = 52 from the second row.

Since 14 6=

52 , there cannot be a solution.

The system is inconsistent and has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 14 / 39

Page 145: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 11: Problem with parameters

x1 + hx2 = −3−2x1 + 4x2 = 6

have (1) a unique solution, (2) infinitely many solution, and (3) no solution?Solution: »

1 h | −3−2 4 | 6

2R1 + R2−−−−−−→

»1 h | −30 2h + 4 | 0

The last row gives the equation: (2h + 4)x2 = 0.

If 2h + 4 = 0 (i.e., h = −2), then 0 · x2 = 0, x2 is a free variable.Then x2 = t , and x1 = −3− hx2 = −3− 2t .If 2h + 4 6= 0 (i.e., h 6= −2 ), then the only way to satisfy thisequation is x2 = 0, then x1 = −3.

Hence,

if h 6= −2, then there is a unique solution.if h = −2, then there are infinitely many solutions.There is so such of value of h so that the system has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 15 / 39

Page 146: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 11: Problem with parameters

x1 + hx2 = −3−2x1 + 4x2 = 6

have (1) a unique solution, (2) infinitely many solution, and (3) no solution?Solution: »

1 h | −3−2 4 | 6

–2R1 + R2−−−−−−→

»1 h | −30 2h + 4 | 0

The last row gives the equation: (2h + 4)x2 = 0.

If 2h + 4 = 0 (i.e., h = −2), then 0 · x2 = 0, x2 is a free variable.Then x2 = t , and x1 = −3− hx2 = −3− 2t .If 2h + 4 6= 0 (i.e., h 6= −2 ), then the only way to satisfy thisequation is x2 = 0, then x1 = −3.

Hence,

if h 6= −2, then there is a unique solution.if h = −2, then there are infinitely many solutions.There is so such of value of h so that the system has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 15 / 39

Page 147: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 11: Problem with parameters

x1 + hx2 = −3−2x1 + 4x2 = 6

have (1) a unique solution, (2) infinitely many solution, and (3) no solution?Solution: »

1 h | −3−2 4 | 6

–2R1 + R2−−−−−−→

»1 h | −3

0 2h + 4 | 0

The last row gives the equation: (2h + 4)x2 = 0.

If 2h + 4 = 0 (i.e., h = −2), then 0 · x2 = 0, x2 is a free variable.Then x2 = t , and x1 = −3− hx2 = −3− 2t .If 2h + 4 6= 0 (i.e., h 6= −2 ), then the only way to satisfy thisequation is x2 = 0, then x1 = −3.

Hence,

if h 6= −2, then there is a unique solution.if h = −2, then there are infinitely many solutions.There is so such of value of h so that the system has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 15 / 39

Page 148: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 11: Problem with parameters

x1 + hx2 = −3−2x1 + 4x2 = 6

have (1) a unique solution, (2) infinitely many solution, and (3) no solution?Solution: »

1 h | −3−2 4 | 6

–2R1 + R2−−−−−−→

»1 h | −30

2h + 4 | 0

The last row gives the equation: (2h + 4)x2 = 0.

If 2h + 4 = 0 (i.e., h = −2), then 0 · x2 = 0, x2 is a free variable.Then x2 = t , and x1 = −3− hx2 = −3− 2t .If 2h + 4 6= 0 (i.e., h 6= −2 ), then the only way to satisfy thisequation is x2 = 0, then x1 = −3.

Hence,

if h 6= −2, then there is a unique solution.if h = −2, then there are infinitely many solutions.There is so such of value of h so that the system has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 15 / 39

Page 149: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 11: Problem with parameters

x1 + hx2 = −3−2x1 + 4x2 = 6

have (1) a unique solution, (2) infinitely many solution, and (3) no solution?Solution: »

1 h | −3−2 4 | 6

–2R1 + R2−−−−−−→

»1 h | −30 2h + 4 |

0

The last row gives the equation: (2h + 4)x2 = 0.

If 2h + 4 = 0 (i.e., h = −2), then 0 · x2 = 0, x2 is a free variable.Then x2 = t , and x1 = −3− hx2 = −3− 2t .If 2h + 4 6= 0 (i.e., h 6= −2 ), then the only way to satisfy thisequation is x2 = 0, then x1 = −3.

Hence,

if h 6= −2, then there is a unique solution.if h = −2, then there are infinitely many solutions.There is so such of value of h so that the system has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 15 / 39

Page 150: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 11: Problem with parameters

x1 + hx2 = −3−2x1 + 4x2 = 6

have (1) a unique solution, (2) infinitely many solution, and (3) no solution?Solution: »

1 h | −3−2 4 | 6

–2R1 + R2−−−−−−→

»1 h | −30 2h + 4 | 0

The last row gives the equation: (2h + 4)x2 = 0.

If 2h + 4 = 0 (i.e., h = −2), then 0 · x2 = 0, x2 is a free variable.Then x2 = t , and x1 = −3− hx2 = −3− 2t .If 2h + 4 6= 0 (i.e., h 6= −2 ), then the only way to satisfy thisequation is x2 = 0, then x1 = −3.

Hence,

if h 6= −2, then there is a unique solution.if h = −2, then there are infinitely many solutions.There is so such of value of h so that the system has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 15 / 39

Page 151: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 11: Problem with parameters

x1 + hx2 = −3−2x1 + 4x2 = 6

have (1) a unique solution, (2) infinitely many solution, and (3) no solution?Solution: »

1 h | −3−2 4 | 6

–2R1 + R2−−−−−−→

»1 h | −30 2h + 4 | 0

The last row gives the equation: (2h + 4)x2 = 0.

If 2h + 4 = 0 (i.e., h = −2), then 0 · x2 = 0, x2 is a free variable.Then x2 = t , and x1 = −3− hx2 = −3− 2t .If 2h + 4 6= 0 (i.e., h 6= −2 ), then the only way to satisfy thisequation is x2 = 0, then x1 = −3.

Hence,

if h 6= −2, then there is a unique solution.if h = −2, then there are infinitely many solutions.There is so such of value of h so that the system has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 15 / 39

Page 152: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 11: Problem with parameters

x1 + hx2 = −3−2x1 + 4x2 = 6

have (1) a unique solution, (2) infinitely many solution, and (3) no solution?Solution: »

1 h | −3−2 4 | 6

–2R1 + R2−−−−−−→

»1 h | −30 2h + 4 | 0

The last row gives the equation: (2h + 4)x2 = 0.

If 2h + 4 = 0 (i.e., h = −2), then 0 · x2 = 0, x2 is a free variable.Then x2 = t , and x1 = −3− hx2 = −3− 2t .

If 2h + 4 6= 0 (i.e., h 6= −2 ), then the only way to satisfy thisequation is x2 = 0, then x1 = −3.

Hence,

if h 6= −2, then there is a unique solution.if h = −2, then there are infinitely many solutions.There is so such of value of h so that the system has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 15 / 39

Page 153: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 11: Problem with parameters

x1 + hx2 = −3−2x1 + 4x2 = 6

have (1) a unique solution, (2) infinitely many solution, and (3) no solution?Solution: »

1 h | −3−2 4 | 6

–2R1 + R2−−−−−−→

»1 h | −30 2h + 4 | 0

The last row gives the equation: (2h + 4)x2 = 0.

If 2h + 4 = 0 (i.e., h = −2), then 0 · x2 = 0, x2 is a free variable.Then x2 = t , and x1 = −3− hx2 = −3− 2t .If 2h + 4 6= 0 (i.e., h 6= −2 ), then the only way to satisfy thisequation is x2 = 0, then x1 = −3.

Hence,

if h 6= −2, then there is a unique solution.if h = −2, then there are infinitely many solutions.There is so such of value of h so that the system has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 15 / 39

Page 154: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 11: Problem with parameters

x1 + hx2 = −3−2x1 + 4x2 = 6

have (1) a unique solution, (2) infinitely many solution, and (3) no solution?Solution: »

1 h | −3−2 4 | 6

–2R1 + R2−−−−−−→

»1 h | −30 2h + 4 | 0

The last row gives the equation: (2h + 4)x2 = 0.

If 2h + 4 = 0 (i.e., h = −2), then 0 · x2 = 0, x2 is a free variable.Then x2 = t , and x1 = −3− hx2 = −3− 2t .If 2h + 4 6= 0 (i.e., h 6= −2 ), then the only way to satisfy thisequation is x2 = 0, then x1 = −3.

Hence,

if h 6= −2, then there is a unique solution.if h = −2, then there are infinitely many solutions.There is so such of value of h so that the system has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 15 / 39

Page 155: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 11: Problem with parameters

x1 + hx2 = −3−2x1 + 4x2 = 6

have (1) a unique solution, (2) infinitely many solution, and (3) no solution?Solution: »

1 h | −3−2 4 | 6

–2R1 + R2−−−−−−→

»1 h | −30 2h + 4 | 0

The last row gives the equation: (2h + 4)x2 = 0.

If 2h + 4 = 0 (i.e., h = −2), then 0 · x2 = 0, x2 is a free variable.Then x2 = t , and x1 = −3− hx2 = −3− 2t .If 2h + 4 6= 0 (i.e., h 6= −2 ), then the only way to satisfy thisequation is x2 = 0, then x1 = −3.

Hence,

if h 6= −2, then there is a unique solution.

if h = −2, then there are infinitely many solutions.There is so such of value of h so that the system has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 15 / 39

Page 156: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 11: Problem with parameters

x1 + hx2 = −3−2x1 + 4x2 = 6

have (1) a unique solution, (2) infinitely many solution, and (3) no solution?Solution: »

1 h | −3−2 4 | 6

–2R1 + R2−−−−−−→

»1 h | −30 2h + 4 | 0

The last row gives the equation: (2h + 4)x2 = 0.

If 2h + 4 = 0 (i.e., h = −2), then 0 · x2 = 0, x2 is a free variable.Then x2 = t , and x1 = −3− hx2 = −3− 2t .If 2h + 4 6= 0 (i.e., h 6= −2 ), then the only way to satisfy thisequation is x2 = 0, then x1 = −3.

Hence,

if h 6= −2, then there is a unique solution.if h = −2, then there are infinitely many solutions.

There is so such of value of h so that the system has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 15 / 39

Page 157: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra I: Solving Linear Systems of Equations

Example 11: Problem with parameters

x1 + hx2 = −3−2x1 + 4x2 = 6

have (1) a unique solution, (2) infinitely many solution, and (3) no solution?Solution: »

1 h | −3−2 4 | 6

–2R1 + R2−−−−−−→

»1 h | −30 2h + 4 | 0

The last row gives the equation: (2h + 4)x2 = 0.

If 2h + 4 = 0 (i.e., h = −2), then 0 · x2 = 0, x2 is a free variable.Then x2 = t , and x1 = −3− hx2 = −3− 2t .If 2h + 4 6= 0 (i.e., h 6= −2 ), then the only way to satisfy thisequation is x2 = 0, then x1 = −3.

Hence,

if h 6= −2, then there is a unique solution.if h = −2, then there are infinitely many solutions.There is so such of value of h so that the system has no solution.

Jing Li (UofO) MAT 1332 C March 8, 2010 15 / 39

Page 158: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

Vectors and Matrices

Definition

A matrix is a rectangular array of numbers A =

26664a11 a12 · · · a1n

a21 a22 · · · a2n...

.... . .

...am1 am2 · · · amn

37775 .

The elements aij of the matrix A are called entries. If the matrix has m rows and ncolumns, it is called an m × n matrix.

Note:

If the size of the matrix A is clear, we can use the shorthand notation A = [aij ].

If m = n, then A is a square matrix. eg. a 3× 3 square matrix:

24−1 3 00 1 −15 4 3

35If A is a square matrix, then the elements aii are called the diagonal elementsand their sum are called the trace of A.

For the above example, the diagonal elements are −1, 1, 3, and the trace istr(A) = −1 + 1 + 3 = 3.

Jing Li (UofO) MAT 1332 C March 8, 2010 16 / 39

Page 159: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

Vectors and Matrices

Definition

A matrix is a rectangular array of numbers A =

26664a11 a12 · · · a1n

a21 a22 · · · a2n...

.... . .

...am1 am2 · · · amn

37775 .

The elements aij of the matrix A are called entries. If the matrix has m rows and ncolumns, it is called an m × n matrix.

Note:

If the size of the matrix A is clear, we can use the shorthand notation A = [aij ].

If m = n, then A is a square matrix. eg. a 3× 3 square matrix:

24−1 3 00 1 −15 4 3

35If A is a square matrix, then the elements aii are called the diagonal elementsand their sum are called the trace of A.

For the above example, the diagonal elements are −1, 1, 3, and the trace istr(A) = −1 + 1 + 3 = 3.

Jing Li (UofO) MAT 1332 C March 8, 2010 16 / 39

Page 160: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

Vectors and Matrices

Definition

A matrix is a rectangular array of numbers A =

26664a11 a12 · · · a1n

a21 a22 · · · a2n...

.... . .

...am1 am2 · · · amn

37775 .

The elements aij of the matrix A are called entries. If the matrix has m rows and ncolumns, it is called an m × n matrix.

Note:

If the size of the matrix A is clear, we can use the shorthand notation A = [aij ].

If m = n, then A is a square matrix. eg. a 3× 3 square matrix:

24−1 3 00 1 −15 4 3

35If A is a square matrix, then the elements aii are called the diagonal elementsand their sum are called the trace of A.

For the above example, the diagonal elements are −1, 1, 3, and the trace istr(A) = −1 + 1 + 3 = 3.

Jing Li (UofO) MAT 1332 C March 8, 2010 16 / 39

Page 161: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

Vectors and Matrices

Definition

A matrix is a rectangular array of numbers A =

26664a11 a12 · · · a1n

a21 a22 · · · a2n...

.... . .

...am1 am2 · · · amn

37775 .

The elements aij of the matrix A are called entries. If the matrix has m rows and ncolumns, it is called an m × n matrix.

Note:

If the size of the matrix A is clear, we can use the shorthand notation A = [aij ].

If m = n, then A is a square matrix. eg. a 3× 3 square matrix:

24−1 3 00 1 −15 4 3

35

If A is a square matrix, then the elements aii are called the diagonal elementsand their sum are called the trace of A.

For the above example, the diagonal elements are −1, 1, 3, and the trace istr(A) = −1 + 1 + 3 = 3.

Jing Li (UofO) MAT 1332 C March 8, 2010 16 / 39

Page 162: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

Vectors and Matrices

Definition

A matrix is a rectangular array of numbers A =

26664a11 a12 · · · a1n

a21 a22 · · · a2n...

.... . .

...am1 am2 · · · amn

37775 .

The elements aij of the matrix A are called entries. If the matrix has m rows and ncolumns, it is called an m × n matrix.

Note:

If the size of the matrix A is clear, we can use the shorthand notation A = [aij ].

If m = n, then A is a square matrix. eg. a 3× 3 square matrix:

24−1 3 00 1 −15 4 3

35If A is a square matrix, then the elements aii are called the diagonal elementsand their sum are called the trace of A.

For the above example, the diagonal elements are −1, 1, 3, and the trace istr(A) = −1 + 1 + 3 = 3.

Jing Li (UofO) MAT 1332 C March 8, 2010 16 / 39

Page 163: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

Vectors and Matrices

Definition

A matrix is a rectangular array of numbers A =

26664a11 a12 · · · a1n

a21 a22 · · · a2n...

.... . .

...am1 am2 · · · amn

37775 .

The elements aij of the matrix A are called entries. If the matrix has m rows and ncolumns, it is called an m × n matrix.

Note:

If the size of the matrix A is clear, we can use the shorthand notation A = [aij ].

If m = n, then A is a square matrix. eg. a 3× 3 square matrix:

24−1 3 00 1 −15 4 3

35If A is a square matrix, then the elements aii are called the diagonal elementsand their sum are called the trace of A.

For the above example,

the diagonal elements are −1, 1, 3, and the trace istr(A) = −1 + 1 + 3 = 3.

Jing Li (UofO) MAT 1332 C March 8, 2010 16 / 39

Page 164: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

Vectors and Matrices

Definition

A matrix is a rectangular array of numbers A =

26664a11 a12 · · · a1n

a21 a22 · · · a2n...

.... . .

...am1 am2 · · · amn

37775 .

The elements aij of the matrix A are called entries. If the matrix has m rows and ncolumns, it is called an m × n matrix.

Note:

If the size of the matrix A is clear, we can use the shorthand notation A = [aij ].

If m = n, then A is a square matrix. eg. a 3× 3 square matrix:

24−1 3 00 1 −15 4 3

35If A is a square matrix, then the elements aii are called the diagonal elementsand their sum are called the trace of A.

For the above example, the diagonal elements are −1, 1, 3,

and the trace istr(A) = −1 + 1 + 3 = 3.

Jing Li (UofO) MAT 1332 C March 8, 2010 16 / 39

Page 165: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

Vectors and Matrices

Definition

A matrix is a rectangular array of numbers A =

26664a11 a12 · · · a1n

a21 a22 · · · a2n...

.... . .

...am1 am2 · · · amn

37775 .

The elements aij of the matrix A are called entries. If the matrix has m rows and ncolumns, it is called an m × n matrix.

Note:

If the size of the matrix A is clear, we can use the shorthand notation A = [aij ].

If m = n, then A is a square matrix. eg. a 3× 3 square matrix:

24−1 3 00 1 −15 4 3

35If A is a square matrix, then the elements aii are called the diagonal elementsand their sum are called the trace of A.

For the above example, the diagonal elements are −1, 1, 3, and the trace istr(A) = −1 + 1 + 3 = 3.

Jing Li (UofO) MAT 1332 C March 8, 2010 16 / 39

Page 166: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

A 1× n matrix is called a row vector: [c1, c2, · · · , cn]. eg. a 1× 4 row vector:[1 3 0 5].

An m × 1 matrix is called a column vector:

26664b1

b2...

bm

37775. eg. a 3× 1 column vector:

24274

35.

Two matrices A = [aij ], B = [bij ] are said to be equal if they have the samedimension and if for all i, j , we have aij = bij .

the zero matrix:

2640 · · · 0...

. . ....

0 · · · 0

375, the size of this matrix is usually clear from the

context.

the identity matrix:

266641 0 · · · 00 1 · · · 0...

.... . .

...0 0 · · · 1

37775, the size of this matrix is usually clear

from the context.

Jing Li (UofO) MAT 1332 C March 8, 2010 17 / 39

Page 167: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

A 1× n matrix is called a row vector: [c1, c2, · · · , cn]. eg. a 1× 4 row vector:[1 3 0 5].

An m × 1 matrix is called a column vector:

26664b1

b2...

bm

37775. eg. a 3× 1 column vector:

24274

35.

Two matrices A = [aij ], B = [bij ] are said to be equal if they have the samedimension and if for all i, j , we have aij = bij .

the zero matrix:

2640 · · · 0...

. . ....

0 · · · 0

375, the size of this matrix is usually clear from the

context.

the identity matrix:

266641 0 · · · 00 1 · · · 0...

.... . .

...0 0 · · · 1

37775, the size of this matrix is usually clear

from the context.

Jing Li (UofO) MAT 1332 C March 8, 2010 17 / 39

Page 168: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

A 1× n matrix is called a row vector: [c1, c2, · · · , cn]. eg. a 1× 4 row vector:[1 3 0 5].

An m × 1 matrix is called a column vector:

26664b1

b2...

bm

37775. eg. a 3× 1 column vector:

24274

35.

Two matrices A = [aij ], B = [bij ] are said to be equal if they have the samedimension and if for all i, j , we have aij = bij .

the zero matrix:

2640 · · · 0...

. . ....

0 · · · 0

375, the size of this matrix is usually clear from the

context.

the identity matrix:

266641 0 · · · 00 1 · · · 0...

.... . .

...0 0 · · · 1

37775, the size of this matrix is usually clear

from the context.

Jing Li (UofO) MAT 1332 C March 8, 2010 17 / 39

Page 169: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

A 1× n matrix is called a row vector: [c1, c2, · · · , cn]. eg. a 1× 4 row vector:[1 3 0 5].

An m × 1 matrix is called a column vector:

26664b1

b2...

bm

37775. eg. a 3× 1 column vector:

24274

35.

Two matrices A = [aij ], B = [bij ] are said to be equal if they have the samedimension and if for all i, j , we have aij = bij .

the zero matrix:

2640 · · · 0...

. . ....

0 · · · 0

375, the size of this matrix is usually clear from the

context.

the identity matrix:

266641 0 · · · 00 1 · · · 0...

.... . .

...0 0 · · · 1

37775, the size of this matrix is usually clear

from the context.

Jing Li (UofO) MAT 1332 C March 8, 2010 17 / 39

Page 170: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

A 1× n matrix is called a row vector: [c1, c2, · · · , cn]. eg. a 1× 4 row vector:[1 3 0 5].

An m × 1 matrix is called a column vector:

26664b1

b2...

bm

37775. eg. a 3× 1 column vector:

24274

35.

Two matrices A = [aij ], B = [bij ] are said to be equal if they have the samedimension and if for all i, j , we have aij = bij .

the zero matrix:

2640 · · · 0...

. . ....

0 · · · 0

375, the size of this matrix is usually clear from the

context.

the identity matrix:

266641 0 · · · 00 1 · · · 0...

.... . .

...0 0 · · · 1

37775, the size of this matrix is usually clear

from the context.

Jing Li (UofO) MAT 1332 C March 8, 2010 17 / 39

Page 171: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Definition

A 1× n matrix is called a row vector: [c1, c2, · · · , cn]. eg. a 1× 4 row vector:[1 3 0 5].

An m × 1 matrix is called a column vector:

26664b1

b2...

bm

37775. eg. a 3× 1 column vector:

24274

35.

Two matrices A = [aij ], B = [bij ] are said to be equal if they have the samedimension and if for all i, j , we have aij = bij .

the zero matrix:

2640 · · · 0...

. . ....

0 · · · 0

375, the size of this matrix is usually clear from the

context.

the identity matrix:

266641 0 · · · 00 1 · · · 0...

.... . .

...0 0 · · · 1

37775, the size of this matrix is usually clear

from the context.Jing Li (UofO) MAT 1332 C March 8, 2010 17 / 39

Page 172: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Matrix Addition and Scalar Multiplication

Definition

Matrix AdditionSuppose that A = [aij ] and B = [bij ] are two m × n matrices. Then

C = A + B

is an m × n matrix with entries

cij = aij + bij , for 1 ≤ i ≤ m, 1 ≤ j ≤ n.

Definition

Scalar MultiplicationSuppose that A = [aij ] is an m × n matrix and k is a number. Then

kA = [kaij ]

for 1 ≤ i ≤ m and 1 ≤ j ≤ n.

Jing Li (UofO) MAT 1332 C March 8, 2010 18 / 39

Page 173: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Matrix Addition and Scalar Multiplication

Definition

Matrix AdditionSuppose that A = [aij ] and B = [bij ] are two m × n matrices. Then

C = A + B

is an m × n matrix with entries

cij = aij + bij , for 1 ≤ i ≤ m, 1 ≤ j ≤ n.

Definition

Scalar MultiplicationSuppose that A = [aij ] is an m × n matrix and k is a number. Then

kA = [kaij ]

for 1 ≤ i ≤ m and 1 ≤ j ≤ n.

Jing Li (UofO) MAT 1332 C March 8, 2010 18 / 39

Page 174: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Note:

What are the sizes of C = A + B and kA?

C is an m × n matrix.kA is the same size as A. (m × n matrix)

In the field of Linear Algebra, a number is often called a scalar.

Jing Li (UofO) MAT 1332 C March 8, 2010 19 / 39

Page 175: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Note:

What are the sizes of C = A + B and kA?

C is an m × n matrix.kA is the same size as A. (m × n matrix)

In the field of Linear Algebra, a number is often called a scalar.

Jing Li (UofO) MAT 1332 C March 8, 2010 19 / 39

Page 176: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Note:

What are the sizes of C = A + B and kA?

C is an m × n matrix.kA is the same size as A. (m × n matrix)

In the field of Linear Algebra, a number is often called a scalar.

Jing Li (UofO) MAT 1332 C March 8, 2010 19 / 39

Page 177: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Note:

What are the sizes of C = A + B and kA?

C is an m × n matrix.kA is the same size as A. (m × n matrix)

In the field of Linear Algebra, a number is often called a scalar.

Jing Li (UofO) MAT 1332 C March 8, 2010 19 / 39

Page 178: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.

Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 179: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 180: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»

8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 181: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8

10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 182: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10

123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 183: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 12

3 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 184: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123

3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 185: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3

3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 186: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 187: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»

− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 188: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6

− 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 189: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6

− 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 190: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 6

5 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 191: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65

7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 192: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7

9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 193: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 194: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»

5 10 1520 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 195: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5

10 1520 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 196: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10

1520 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 197: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 198: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20

25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 199: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25

30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 200: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 201: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»

3 6 912 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 202: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3

6 912 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 203: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6

912 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 204: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 205: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12

15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 206: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15

18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 207: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»

14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 208: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14

16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 209: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16

18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 210: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18

− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 211: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2

− 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 212: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4

− 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 213: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»

17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 214: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17

22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 215: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22

2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 216: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 27

10 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 217: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710

11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 218: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11

12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 219: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 1: Consider the following two 2× 3-matrices,

A =

»1 2 34 5 6

–, B =

»7 8 9−1 −2 −3

–,

Find A + B, A− B, 5A, 3A + 2B.Solution:

1 A + B =

»1 2 34 5 6

–+

»7 8 9−1 −2 −3

–=

»8 10 123 3 3

–2 A− B =

»1 2 34 5 6

–−»

7 8 9−1 −2 −3

–=

»− 6 − 6 − 65 7 9

–3 5A =

»5 10 15

20 25 30

–4 3A + 2B =

»3 6 9

12 15 18

–+

»14 16 18− 2 − 4 − 6

–=

»17 22 2710 11 12

Jing Li (UofO) MAT 1332 C March 8, 2010 20 / 39

Page 220: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

The Transpose of a Matrix

Definition

Suppose that A = [aij ] is an m × n matrix. The transpose of A, denoted by AT , is ann ×m matrix with entries

AT = [aji ]

which is obtained from A by interchanging rows and columns, or, loosely speaking, byflipping the matrix along this diagonal.

Note:What is the size of AT ? – AT is n ×m if A is m × n.What is the transpose of a column vector? – a row vector.What is the transpose of a row vector? – a column vector.Like wise, the first column of AT is the first row of A; the second column of AT isthe second row of A, etc..

If AT = A, then A is symmetric. e.g. A =

24 1 2 32 4 63 6 5

35, then AT = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 21 / 39

Page 221: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

The Transpose of a Matrix

Definition

Suppose that A = [aij ] is an m × n matrix. The transpose of A, denoted by AT , is ann ×m matrix with entries

AT = [aji ]

which is obtained from A by interchanging rows and columns, or, loosely speaking, byflipping the matrix along this diagonal.

Note:

What is the size of AT ? – AT is n ×m if A is m × n.What is the transpose of a column vector? – a row vector.What is the transpose of a row vector? – a column vector.Like wise, the first column of AT is the first row of A; the second column of AT isthe second row of A, etc..

If AT = A, then A is symmetric. e.g. A =

24 1 2 32 4 63 6 5

35, then AT = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 21 / 39

Page 222: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

The Transpose of a Matrix

Definition

Suppose that A = [aij ] is an m × n matrix. The transpose of A, denoted by AT , is ann ×m matrix with entries

AT = [aji ]

which is obtained from A by interchanging rows and columns, or, loosely speaking, byflipping the matrix along this diagonal.

Note:What is the size of AT ? –

AT is n ×m if A is m × n.What is the transpose of a column vector? – a row vector.What is the transpose of a row vector? – a column vector.Like wise, the first column of AT is the first row of A; the second column of AT isthe second row of A, etc..

If AT = A, then A is symmetric. e.g. A =

24 1 2 32 4 63 6 5

35, then AT = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 21 / 39

Page 223: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

The Transpose of a Matrix

Definition

Suppose that A = [aij ] is an m × n matrix. The transpose of A, denoted by AT , is ann ×m matrix with entries

AT = [aji ]

which is obtained from A by interchanging rows and columns, or, loosely speaking, byflipping the matrix along this diagonal.

Note:What is the size of AT ? – AT is n ×m if A is m × n.

What is the transpose of a column vector? – a row vector.What is the transpose of a row vector? – a column vector.Like wise, the first column of AT is the first row of A; the second column of AT isthe second row of A, etc..

If AT = A, then A is symmetric. e.g. A =

24 1 2 32 4 63 6 5

35, then AT = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 21 / 39

Page 224: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

The Transpose of a Matrix

Definition

Suppose that A = [aij ] is an m × n matrix. The transpose of A, denoted by AT , is ann ×m matrix with entries

AT = [aji ]

which is obtained from A by interchanging rows and columns, or, loosely speaking, byflipping the matrix along this diagonal.

Note:What is the size of AT ? – AT is n ×m if A is m × n.What is the transpose of a column vector? –

a row vector.What is the transpose of a row vector? – a column vector.Like wise, the first column of AT is the first row of A; the second column of AT isthe second row of A, etc..

If AT = A, then A is symmetric. e.g. A =

24 1 2 32 4 63 6 5

35, then AT = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 21 / 39

Page 225: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

The Transpose of a Matrix

Definition

Suppose that A = [aij ] is an m × n matrix. The transpose of A, denoted by AT , is ann ×m matrix with entries

AT = [aji ]

which is obtained from A by interchanging rows and columns, or, loosely speaking, byflipping the matrix along this diagonal.

Note:What is the size of AT ? – AT is n ×m if A is m × n.What is the transpose of a column vector? – a row vector.

What is the transpose of a row vector? – a column vector.Like wise, the first column of AT is the first row of A; the second column of AT isthe second row of A, etc..

If AT = A, then A is symmetric. e.g. A =

24 1 2 32 4 63 6 5

35, then AT = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 21 / 39

Page 226: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

The Transpose of a Matrix

Definition

Suppose that A = [aij ] is an m × n matrix. The transpose of A, denoted by AT , is ann ×m matrix with entries

AT = [aji ]

which is obtained from A by interchanging rows and columns, or, loosely speaking, byflipping the matrix along this diagonal.

Note:What is the size of AT ? – AT is n ×m if A is m × n.What is the transpose of a column vector? – a row vector.What is the transpose of a row vector? –

a column vector.Like wise, the first column of AT is the first row of A; the second column of AT isthe second row of A, etc..

If AT = A, then A is symmetric. e.g. A =

24 1 2 32 4 63 6 5

35, then AT = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 21 / 39

Page 227: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

The Transpose of a Matrix

Definition

Suppose that A = [aij ] is an m × n matrix. The transpose of A, denoted by AT , is ann ×m matrix with entries

AT = [aji ]

which is obtained from A by interchanging rows and columns, or, loosely speaking, byflipping the matrix along this diagonal.

Note:What is the size of AT ? – AT is n ×m if A is m × n.What is the transpose of a column vector? – a row vector.What is the transpose of a row vector? – a column vector.

Like wise, the first column of AT is the first row of A; the second column of AT isthe second row of A, etc..

If AT = A, then A is symmetric. e.g. A =

24 1 2 32 4 63 6 5

35, then AT = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 21 / 39

Page 228: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

The Transpose of a Matrix

Definition

Suppose that A = [aij ] is an m × n matrix. The transpose of A, denoted by AT , is ann ×m matrix with entries

AT = [aji ]

which is obtained from A by interchanging rows and columns, or, loosely speaking, byflipping the matrix along this diagonal.

Note:What is the size of AT ? – AT is n ×m if A is m × n.What is the transpose of a column vector? – a row vector.What is the transpose of a row vector? – a column vector.Like wise, the first column of AT is the first row of A; the second column of AT isthe second row of A, etc..

If AT = A, then A is symmetric. e.g. A =

24 1 2 32 4 63 6 5

35, then AT = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 21 / 39

Page 229: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

The Transpose of a Matrix

Definition

Suppose that A = [aij ] is an m × n matrix. The transpose of A, denoted by AT , is ann ×m matrix with entries

AT = [aji ]

which is obtained from A by interchanging rows and columns, or, loosely speaking, byflipping the matrix along this diagonal.

Note:What is the size of AT ? – AT is n ×m if A is m × n.What is the transpose of a column vector? – a row vector.What is the transpose of a row vector? – a column vector.Like wise, the first column of AT is the first row of A; the second column of AT isthe second row of A, etc..

If AT = A, then A is symmetric.

e.g. A =

24 1 2 32 4 63 6 5

35, then AT = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 21 / 39

Page 230: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

The Transpose of a Matrix

Definition

Suppose that A = [aij ] is an m × n matrix. The transpose of A, denoted by AT , is ann ×m matrix with entries

AT = [aji ]

which is obtained from A by interchanging rows and columns, or, loosely speaking, byflipping the matrix along this diagonal.

Note:What is the size of AT ? – AT is n ×m if A is m × n.What is the transpose of a column vector? – a row vector.What is the transpose of a row vector? – a column vector.Like wise, the first column of AT is the first row of A; the second column of AT isthe second row of A, etc..

If AT = A, then A is symmetric. e.g. A =

24 1 2 32 4 63 6 5

35, then AT = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 21 / 39

Page 231: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 2: Consider

A =

»1 2 34 5 6

–, B =

ˆ1 2

˜, C =

»45

–,

Find AT , BT , CT .Solution:

AT =

24 1 42 53 6

35BT =

»12

–CT =

ˆ4 5

˜

Jing Li (UofO) MAT 1332 C March 8, 2010 22 / 39

Page 232: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 2: Consider

A =

»1 2 34 5 6

–, B =

ˆ1 2

˜, C =

»45

–,

Find AT , BT , CT .Solution:

AT =

24 1 42 53 6

35

BT =

»12

–CT =

ˆ4 5

˜

Jing Li (UofO) MAT 1332 C March 8, 2010 22 / 39

Page 233: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 2: Consider

A =

»1 2 34 5 6

–, B =

ˆ1 2

˜, C =

»45

–,

Find AT , BT , CT .Solution:

AT =

24 1 42 53 6

35BT =

»12

CT =ˆ

4 5˜

Jing Li (UofO) MAT 1332 C March 8, 2010 22 / 39

Page 234: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 2: Consider

A =

»1 2 34 5 6

–, B =

ˆ1 2

˜, C =

»45

–,

Find AT , BT , CT .Solution:

AT =

24 1 42 53 6

35BT =

»12

–CT =

ˆ4 5

˜

Jing Li (UofO) MAT 1332 C March 8, 2010 22 / 39

Page 235: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Matrix-Vector Multiplication

Definition

If the matrix A has n columns and the column vector x has n rows, then the productAx is defined as follows:

Ax =

26664a11x1 + a12x2 + · · ·+ a1nxn

a21x1 + a22x2 + · · ·+ a2nxn...

am1x1 + am2x2 + · · ·+ amnxn

37775where,

A =

26664a11 a12 · · · a1n

a21 a22 · · · a2n...

.... . .

...am1 am2 · · · amn

37775 , x =

26664x1

x2...

xn

37775 .

Jing Li (UofO) MAT 1332 C March 8, 2010 23 / 39

Page 236: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Matrix-Vector Multiplication

Note:

How to remember this definition?

simply think about linear systems of equations: Ax = b,a11x1 + a12x2 + · · ·+ a1nxn = b1a21x1 + a22x2 + · · ·+ a2nxn = b2

...am1x1 + am2x2 + · · ·+ amnxn = bm

A: coefficient matrix of the linear system.

x =

x1x2...

xn

, unknown variable column vector. b =

b1b2...

bm

.

What is the size of Ax? –m × 1 column vector.

Jing Li (UofO) MAT 1332 C March 8, 2010 24 / 39

Page 237: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Matrix-Vector Multiplication

Note:

How to remember this definition?

simply think about linear systems of equations: Ax = b,a11x1 + a12x2 + · · ·+ a1nxn = b1a21x1 + a22x2 + · · ·+ a2nxn = b2

...am1x1 + am2x2 + · · ·+ amnxn = bm

A: coefficient matrix of the linear system.

x =

x1x2...

xn

, unknown variable column vector. b =

b1b2...

bm

.

What is the size of Ax? –m × 1 column vector.

Jing Li (UofO) MAT 1332 C March 8, 2010 24 / 39

Page 238: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Matrix-Vector Multiplication

Note:

How to remember this definition?

simply think about linear systems of equations: Ax = b,a11x1 + a12x2 + · · ·+ a1nxn = b1a21x1 + a22x2 + · · ·+ a2nxn = b2

...am1x1 + am2x2 + · · ·+ amnxn = bm

A: coefficient matrix of the linear system.

x =

x1x2...

xn

, unknown variable column vector. b =

b1b2...

bm

.

What is the size of Ax? –m × 1 column vector.

Jing Li (UofO) MAT 1332 C March 8, 2010 24 / 39

Page 239: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Matrix-Vector Multiplication

Note:

How to remember this definition?

simply think about linear systems of equations: Ax = b,a11x1 + a12x2 + · · ·+ a1nxn = b1a21x1 + a22x2 + · · ·+ a2nxn = b2

...am1x1 + am2x2 + · · ·+ amnxn = bm

A: coefficient matrix of the linear system.

x =

x1x2...

xn

, unknown variable column vector. b =

b1b2...

bm

.

What is the size of Ax? –

m × 1 column vector.

Jing Li (UofO) MAT 1332 C March 8, 2010 24 / 39

Page 240: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Matrix-Vector Multiplication

Note:

How to remember this definition?

simply think about linear systems of equations: Ax = b,a11x1 + a12x2 + · · ·+ a1nxn = b1a21x1 + a22x2 + · · ·+ a2nxn = b2

...am1x1 + am2x2 + · · ·+ amnxn = bm

A: coefficient matrix of the linear system.

x =

x1x2...

xn

, unknown variable column vector. b =

b1b2...

bm

.

What is the size of Ax? –m × 1 column vector.

Jing Li (UofO) MAT 1332 C March 8, 2010 24 / 39

Page 241: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»

1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 242: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1

+ 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 243: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2

+ 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 244: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 3

4 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 245: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1

+ 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 246: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2

+ 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 247: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»

1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 248: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»14

32

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 249: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775

=

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 250: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»

1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 251: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)

4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 252: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 253: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»

724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 254: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»7

24

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 255: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 256: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 257: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

=

6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 258: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 259: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»

1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 260: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 2

4 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 261: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 262: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»

36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 263: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»3

6

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 264: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 265: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24

1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 266: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)

4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 267: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)

3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 268: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35

=

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 269: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24

− 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 270: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3

− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 271: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7

− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 272: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 3: Calculate

1

»1 2 34 5 6

–24 123

35 =

»1 · 1 + 2 · 2 + 3 · 34 · 1 + 5 · 2 + 6 · 3

–=

»1432

2

»1 2 3 74 5 6 8

–2664123−1

3775 =

»1 · 1 + 2 · 2 + 3 · 3 + 7 · (−1)4 · 1 + 5 · 2 + 6 · 3 + 8 · (−1)

–=

»724

1 2 3˜ 24 1−23

35 =ˆ

1 · 1 + 2 · (−2) + 3 · 3˜

= 6

4

»1 24 5

– »−12

–=

»1 · (−1) + 2 · 24 · (−1) + 5 · 2

–=

»36

5

24 1 24 53 6

35» 1−2

–=

24 1 · 1 + 2 · (−2)4 · 1 + 5 · (−2)3 · 1 + 6 · (−2)

35 =

24 − 3− 7− 9

35

Jing Li (UofO) MAT 1332 C March 8, 2010 25 / 39

Page 273: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Matrix-Matrix Multiplication

Definition

Suppose that A = [aij ] is an m × n matrix and B = [bij ] is an n × k matrix. Then

C = AB

is an m × k matrix with

cij = ai1b1j + ai2b2j + · · ·+ ainbnj =nX

l=1

ailblj

for 1 ≤ i ≤ m and 1 ≤ j ≤ k.

Jing Li (UofO) MAT 1332 C March 8, 2010 26 / 39

Page 274: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Note:

How to understand this definition by using the definition of matrix-vectormultiplication?

We may think of each column of B as a column vector of length n.We know how to multiply each of these with the matrix A and thenwe put the resulting vectors into one matrix.

A =

26664a11 · · · a1n

a21 · · · a2n...

...am1 · · · amn

37775 , B =

26664b11 · · · b1k

b21 · · · b2k...

...bn1 · · · bnk

37775 =ˆ

B1 | B2 | · · · | Bk˜

where, B1 =

264 b11...

bn1

375, B2 =

264 b12...

bn2

375, . . . , B1 =

264 b1k...

bnk

375.

Then AB =ˆ

AB1 | AB2 | · · · | ABk˜

What is the size of matrix C, the product of A and B?

(m × n) ∗ (n× k) = (m× k).

Jing Li (UofO) MAT 1332 C March 8, 2010 27 / 39

Page 275: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Note:

How to understand this definition by using the definition of matrix-vectormultiplication?

We may think of each column of B as a column vector of length n.We know how to multiply each of these with the matrix A and thenwe put the resulting vectors into one matrix.

A =

26664a11 · · · a1n

a21 · · · a2n...

...am1 · · · amn

37775 , B =

26664b11 · · · b1k

b21 · · · b2k...

...bn1 · · · bnk

37775 =ˆ

B1 | B2 | · · · | Bk˜

where, B1 =

264 b11...

bn1

375, B2 =

264 b12...

bn2

375, . . . , B1 =

264 b1k...

bnk

375.

Then AB =ˆ

AB1 | AB2 | · · · | ABk˜

What is the size of matrix C, the product of A and B?

(m × n) ∗ (n× k) = (m× k).

Jing Li (UofO) MAT 1332 C March 8, 2010 27 / 39

Page 276: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4: Consider the following matrices

A =

»1 23 4

–, B =

»1 2 3−1 0 4

–, C =

24 2 11 −1−1 0

35 , D =

»−1 −20 1

–Then, well-defined colored in blue, undefined colored in red.

AB =

»1 23 4

– »1 2 3−1 0 4

–=

»1 · 1 + 2 · (−1) 1 · 2 + 2 · 0 1 · 3 + 2 · 43 · 1 + 4 · (−1) 3 · 2 + 4 · 0 3 · 3 + 4 · 4

–=

»−1 2 9−1 6 25

–BA =

»1 2 3−1 0 4

– »1 23 4

–(2× 3) ∗ (2× 2) wrong.

BT A =

24 1 −12 03 4

35» 1 23 4

=

24 1 · 1 + (−1) · 3 1 · 2 + (−1) · 42 · 1 + 0 · 3 2 · 2 + 0 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

35 =

24 −2 −22 415 22

35

Jing Li (UofO) MAT 1332 C March 8, 2010 28 / 39

Page 277: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4: Consider the following matrices

A =

»1 23 4

–, B =

»1 2 3−1 0 4

–, C =

24 2 11 −1−1 0

35 , D =

»−1 −20 1

–Then, well-defined colored in blue, undefined colored in red.

AB =

»1 23 4

– »1 2 3−1 0 4

–=

»1 · 1 + 2 · (−1) 1 · 2 + 2 · 0 1 · 3 + 2 · 43 · 1 + 4 · (−1) 3 · 2 + 4 · 0 3 · 3 + 4 · 4

–=

»−1 2 9−1 6 25

–BA =

»1 2 3−1 0 4

– »1 23 4

–(2× 3) ∗ (2× 2) wrong.

BT A =

24 1 −12 03 4

35» 1 23 4

=

24 1 · 1 + (−1) · 3 1 · 2 + (−1) · 42 · 1 + 0 · 3 2 · 2 + 0 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

35 =

24 −2 −22 415 22

35

Jing Li (UofO) MAT 1332 C March 8, 2010 28 / 39

Page 278: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4: Consider the following matrices

A =

»1 23 4

–, B =

»1 2 3−1 0 4

–, C =

24 2 11 −1−1 0

35 , D =

»−1 −20 1

–Then, well-defined colored in blue, undefined colored in red.

AB =

»1 23 4

– »1 2 3−1 0 4

–=

»1 · 1 + 2 · (−1) 1 · 2 + 2 · 0 1 · 3 + 2 · 43 · 1 + 4 · (−1) 3 · 2 + 4 · 0 3 · 3 + 4 · 4

–=

»−1 2 9−1 6 25

–BA =

»1 2 3−1 0 4

– »1 23 4

–(2× 3) ∗ (2× 2) wrong.

BT A =

24 1 −12 03 4

35» 1 23 4

=

24 1 · 1 + (−1) · 3 1 · 2 + (−1) · 42 · 1 + 0 · 3 2 · 2 + 0 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

35 =

24 −2 −22 415 22

35

Jing Li (UofO) MAT 1332 C March 8, 2010 28 / 39

Page 279: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4: Consider the following matrices

A =

»1 23 4

–, B =

»1 2 3−1 0 4

–, C =

24 2 11 −1−1 0

35 , D =

»−1 −20 1

–Then, well-defined colored in blue, undefined colored in red.

AB =

»1 23 4

– »1 2 3−1 0 4

–=

»1 · 1 + 2 · (−1) 1 · 2 + 2 · 0 1 · 3 + 2 · 43 · 1 + 4 · (−1) 3 · 2 + 4 · 0 3 · 3 + 4 · 4

–=

»−1 2 9−1 6 25

BA =

»1 2 3−1 0 4

– »1 23 4

–(2× 3) ∗ (2× 2) wrong.

BT A =

24 1 −12 03 4

35» 1 23 4

=

24 1 · 1 + (−1) · 3 1 · 2 + (−1) · 42 · 1 + 0 · 3 2 · 2 + 0 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

35 =

24 −2 −22 415 22

35

Jing Li (UofO) MAT 1332 C March 8, 2010 28 / 39

Page 280: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4: Consider the following matrices

A =

»1 23 4

–, B =

»1 2 3−1 0 4

–, C =

24 2 11 −1−1 0

35 , D =

»−1 −20 1

–Then, well-defined colored in blue, undefined colored in red.

AB =

»1 23 4

– »1 2 3−1 0 4

–=

»1 · 1 + 2 · (−1) 1 · 2 + 2 · 0 1 · 3 + 2 · 43 · 1 + 4 · (−1) 3 · 2 + 4 · 0 3 · 3 + 4 · 4

–=

»−1 2 9−1 6 25

–BA =

»1 2 3−1 0 4

– »1 23 4

(2× 3) ∗ (2× 2) wrong.

BT A =

24 1 −12 03 4

35» 1 23 4

=

24 1 · 1 + (−1) · 3 1 · 2 + (−1) · 42 · 1 + 0 · 3 2 · 2 + 0 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

35 =

24 −2 −22 415 22

35

Jing Li (UofO) MAT 1332 C March 8, 2010 28 / 39

Page 281: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4: Consider the following matrices

A =

»1 23 4

–, B =

»1 2 3−1 0 4

–, C =

24 2 11 −1−1 0

35 , D =

»−1 −20 1

–Then, well-defined colored in blue, undefined colored in red.

AB =

»1 23 4

– »1 2 3−1 0 4

–=

»1 · 1 + 2 · (−1) 1 · 2 + 2 · 0 1 · 3 + 2 · 43 · 1 + 4 · (−1) 3 · 2 + 4 · 0 3 · 3 + 4 · 4

–=

»−1 2 9−1 6 25

–BA =

»1 2 3−1 0 4

– »1 23 4

–(2× 3) ∗ (2× 2) wrong.

BT A =

24 1 −12 03 4

35» 1 23 4

=

24 1 · 1 + (−1) · 3 1 · 2 + (−1) · 42 · 1 + 0 · 3 2 · 2 + 0 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

35 =

24 −2 −22 415 22

35

Jing Li (UofO) MAT 1332 C March 8, 2010 28 / 39

Page 282: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4: Consider the following matrices

A =

»1 23 4

–, B =

»1 2 3−1 0 4

–, C =

24 2 11 −1−1 0

35 , D =

»−1 −20 1

–Then, well-defined colored in blue, undefined colored in red.

AB =

»1 23 4

– »1 2 3−1 0 4

–=

»1 · 1 + 2 · (−1) 1 · 2 + 2 · 0 1 · 3 + 2 · 43 · 1 + 4 · (−1) 3 · 2 + 4 · 0 3 · 3 + 4 · 4

–=

»−1 2 9−1 6 25

–BA =

»1 2 3−1 0 4

– »1 23 4

–(2× 3) ∗ (2× 2) wrong.

BT A =

24 1 −12 03 4

35» 1 23 4

=

24 1 · 1 + (−1) · 3 1 · 2 + (−1) · 42 · 1 + 0 · 3 2 · 2 + 0 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

35 =

24 −2 −22 415 22

35

Jing Li (UofO) MAT 1332 C March 8, 2010 28 / 39

Page 283: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4: Consider the following matrices

A =

»1 23 4

–, B =

»1 2 3−1 0 4

–, C =

24 2 11 −1−1 0

35 , D =

»−1 −20 1

–Then, well-defined colored in blue, undefined colored in red.

AB =

»1 23 4

– »1 2 3−1 0 4

–=

»1 · 1 + 2 · (−1) 1 · 2 + 2 · 0 1 · 3 + 2 · 43 · 1 + 4 · (−1) 3 · 2 + 4 · 0 3 · 3 + 4 · 4

–=

»−1 2 9−1 6 25

–BA =

»1 2 3−1 0 4

– »1 23 4

–(2× 3) ∗ (2× 2) wrong.

BT A =

24 1 −12 03 4

35» 1 23 4

=

24 1 · 1 + (−1) · 3 1 · 2 + (−1) · 42 · 1 + 0 · 3 2 · 2 + 0 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

35 =

24 −2 −22 415 22

35

Jing Li (UofO) MAT 1332 C March 8, 2010 28 / 39

Page 284: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4: Consider the following matrices

A =

»1 23 4

–, B =

»1 2 3−1 0 4

–, C =

24 2 11 −1−1 0

35 , D =

»−1 −20 1

–Then, well-defined colored in blue, undefined colored in red.

AB =

»1 23 4

– »1 2 3−1 0 4

–=

»1 · 1 + 2 · (−1) 1 · 2 + 2 · 0 1 · 3 + 2 · 43 · 1 + 4 · (−1) 3 · 2 + 4 · 0 3 · 3 + 4 · 4

–=

»−1 2 9−1 6 25

–BA =

»1 2 3−1 0 4

– »1 23 4

–(2× 3) ∗ (2× 2) wrong.

BT A =

24 1 −12 03 4

35» 1 23 4

=

24 1 · 1 + (−1) · 3 1 · 2 + (−1) · 42 · 1 + 0 · 3 2 · 2 + 0 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

35 =

24 −2 −22 415 22

35

Jing Li (UofO) MAT 1332 C March 8, 2010 28 / 39

Page 285: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4: Consider the following matrices

A =

»1 23 4

–, B =

»1 2 3−1 0 4

–, C =

24 2 11 −1−1 0

35 , D =

»−1 −20 1

–Then, well-defined colored in blue, undefined colored in red.

AB =

»1 23 4

– »1 2 3−1 0 4

–=

»1 · 1 + 2 · (−1) 1 · 2 + 2 · 0 1 · 3 + 2 · 43 · 1 + 4 · (−1) 3 · 2 + 4 · 0 3 · 3 + 4 · 4

–=

»−1 2 9−1 6 25

–BA =

»1 2 3−1 0 4

– »1 23 4

–(2× 3) ∗ (2× 2) wrong.

BT A =

24 1 −12 03 4

35» 1 23 4

=

24 1 · 1 + (−1) · 3 1 · 2 + (−1) · 42 · 1 + 0 · 3 2 · 2 + 0 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

35 =

24 −2 −22 415 22

35Jing Li (UofO) MAT 1332 C March 8, 2010 28 / 39

Page 286: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

CA =

24 2 11 −1−1 0

35» 1 23 4

=

24 2 · 1 + 1 · 3 2 · 2 + 1 · 41 · 1 + (−1) · 3 1 · 2 + (−1) · 4(−1) · 1 + 0 · 3 (−1) · 2 + 0 · 4

35 =

24 5 8−2 −2−1 −2

35

ACT =

»1 23 4

– »2 1 −11 −1 0

–=

»1 · 2 + 2 · 1 1 · 1 + 2 · (−1) 1 · (−1) + 2 · 03 · 2 + 4 · 1 3 · 1 + 4 · (−1) 3 · (−1) + 4 · 0

–=

»4 −1 −110 −1 −3

Jing Li (UofO) MAT 1332 C March 8, 2010 29 / 39

Page 287: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

CA =

24 2 11 −1−1 0

35» 1 23 4

=

24 2 · 1 + 1 · 3 2 · 2 + 1 · 41 · 1 + (−1) · 3 1 · 2 + (−1) · 4(−1) · 1 + 0 · 3 (−1) · 2 + 0 · 4

35 =

24 5 8−2 −2−1 −2

35

ACT =

»1 23 4

– »2 1 −11 −1 0

–=

»1 · 2 + 2 · 1 1 · 1 + 2 · (−1) 1 · (−1) + 2 · 03 · 2 + 4 · 1 3 · 1 + 4 · (−1) 3 · (−1) + 4 · 0

–=

»4 −1 −110 −1 −3

Jing Li (UofO) MAT 1332 C March 8, 2010 29 / 39

Page 288: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

CA =

24 2 11 −1−1 0

35» 1 23 4

=

24 2 · 1 + 1 · 3 2 · 2 + 1 · 41 · 1 + (−1) · 3 1 · 2 + (−1) · 4(−1) · 1 + 0 · 3 (−1) · 2 + 0 · 4

35 =

24 5 8−2 −2−1 −2

35

ACT =

»1 23 4

– »2 1 −11 −1 0

–=

»1 · 2 + 2 · 1 1 · 1 + 2 · (−1) 1 · (−1) + 2 · 03 · 2 + 4 · 1 3 · 1 + 4 · (−1) 3 · (−1) + 4 · 0

–=

»4 −1 −110 −1 −3

Jing Li (UofO) MAT 1332 C March 8, 2010 29 / 39

Page 289: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

CA =

24 2 11 −1−1 0

35» 1 23 4

=

24 2 · 1 + 1 · 3 2 · 2 + 1 · 41 · 1 + (−1) · 3 1 · 2 + (−1) · 4(−1) · 1 + 0 · 3 (−1) · 2 + 0 · 4

35 =

24 5 8−2 −2−1 −2

35

ACT =

»1 23 4

– »2 1 −11 −1 0

–=

»1 · 2 + 2 · 1 1 · 1 + 2 · (−1) 1 · (−1) + 2 · 03 · 2 + 4 · 1 3 · 1 + 4 · (−1) 3 · (−1) + 4 · 0

–=

»4 −1 −110 −1 −3

Jing Li (UofO) MAT 1332 C March 8, 2010 29 / 39

Page 290: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

CA =

24 2 11 −1−1 0

35» 1 23 4

=

24 2 · 1 + 1 · 3 2 · 2 + 1 · 41 · 1 + (−1) · 3 1 · 2 + (−1) · 4(−1) · 1 + 0 · 3 (−1) · 2 + 0 · 4

35 =

24 5 8−2 −2−1 −2

35

ACT =

»1 23 4

– »2 1 −11 −1 0

–=

»1 · 2 + 2 · 1 1 · 1 + 2 · (−1) 1 · (−1) + 2 · 03 · 2 + 4 · 1 3 · 1 + 4 · (−1) 3 · (−1) + 4 · 0

–=

»4 −1 −110 −1 −3

Jing Li (UofO) MAT 1332 C March 8, 2010 29 / 39

Page 291: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

CA =

24 2 11 −1−1 0

35» 1 23 4

=

24 2 · 1 + 1 · 3 2 · 2 + 1 · 41 · 1 + (−1) · 3 1 · 2 + (−1) · 4(−1) · 1 + 0 · 3 (−1) · 2 + 0 · 4

35 =

24 5 8−2 −2−1 −2

35

ACT =

»1 23 4

– »2 1 −11 −1 0

–=

»1 · 2 + 2 · 1 1 · 1 + 2 · (−1) 1 · (−1) + 2 · 03 · 2 + 4 · 1 3 · 1 + 4 · (−1) 3 · (−1) + 4 · 0

–=

»4 −1 −110 −1 −3

Jing Li (UofO) MAT 1332 C March 8, 2010 29 / 39

Page 292: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

CA =

24 2 11 −1−1 0

35» 1 23 4

=

24 2 · 1 + 1 · 3 2 · 2 + 1 · 41 · 1 + (−1) · 3 1 · 2 + (−1) · 4(−1) · 1 + 0 · 3 (−1) · 2 + 0 · 4

35 =

24 5 8−2 −2−1 −2

35

ACT =

»1 23 4

– »2 1 −11 −1 0

–=

»1 · 2 + 2 · 1 1 · 1 + 2 · (−1) 1 · (−1) + 2 · 03 · 2 + 4 · 1 3 · 1 + 4 · (−1) 3 · (−1) + 4 · 0

–=

»4 −1 −110 −1 −3

Jing Li (UofO) MAT 1332 C March 8, 2010 29 / 39

Page 293: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

A2 = AA =

»1 23 4

– »1 23 4

–=

»1 · 1 + 2 · 3 1 · 2 + 2 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

–=

»7 10

15 22

AD =

»1 23 4

– »−1 −20 1

–=

»1 · (−1) + 2 · 0 1 · (−2) + 2 · 13 · (−1) + 4 · 0 3 · (−2) + 4 · 1

–=

»−1 0−3 −2

DA =

»−1 −20 1

– »1 23 4

–=

»(−1) · 1 + (−2) · 3 (−1) · 2 + (−2) · 4

0 · 1 + 1 · 3 0 · 2 + 1 · 4

–=

»−7 −103 4

Jing Li (UofO) MAT 1332 C March 8, 2010 30 / 39

Page 294: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

A2 = AA =

»1 23 4

– »1 23 4

–=

»1 · 1 + 2 · 3 1 · 2 + 2 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

–=

»7 10

15 22

AD =

»1 23 4

– »−1 −20 1

–=

»1 · (−1) + 2 · 0 1 · (−2) + 2 · 13 · (−1) + 4 · 0 3 · (−2) + 4 · 1

–=

»−1 0−3 −2

DA =

»−1 −20 1

– »1 23 4

–=

»(−1) · 1 + (−2) · 3 (−1) · 2 + (−2) · 4

0 · 1 + 1 · 3 0 · 2 + 1 · 4

–=

»−7 −103 4

Jing Li (UofO) MAT 1332 C March 8, 2010 30 / 39

Page 295: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

A2 = AA =

»1 23 4

– »1 23 4

–=

»1 · 1 + 2 · 3 1 · 2 + 2 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

=

»7 10

15 22

AD =

»1 23 4

– »−1 −20 1

–=

»1 · (−1) + 2 · 0 1 · (−2) + 2 · 13 · (−1) + 4 · 0 3 · (−2) + 4 · 1

–=

»−1 0−3 −2

DA =

»−1 −20 1

– »1 23 4

–=

»(−1) · 1 + (−2) · 3 (−1) · 2 + (−2) · 4

0 · 1 + 1 · 3 0 · 2 + 1 · 4

–=

»−7 −103 4

Jing Li (UofO) MAT 1332 C March 8, 2010 30 / 39

Page 296: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

A2 = AA =

»1 23 4

– »1 23 4

–=

»1 · 1 + 2 · 3 1 · 2 + 2 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

–=

»7 10

15 22

AD =

»1 23 4

– »−1 −20 1

–=

»1 · (−1) + 2 · 0 1 · (−2) + 2 · 13 · (−1) + 4 · 0 3 · (−2) + 4 · 1

–=

»−1 0−3 −2

DA =

»−1 −20 1

– »1 23 4

–=

»(−1) · 1 + (−2) · 3 (−1) · 2 + (−2) · 4

0 · 1 + 1 · 3 0 · 2 + 1 · 4

–=

»−7 −103 4

Jing Li (UofO) MAT 1332 C March 8, 2010 30 / 39

Page 297: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

A2 = AA =

»1 23 4

– »1 23 4

–=

»1 · 1 + 2 · 3 1 · 2 + 2 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

–=

»7 10

15 22

AD =

»1 23 4

– »−1 −20 1

–=

»1 · (−1) + 2 · 0 1 · (−2) + 2 · 13 · (−1) + 4 · 0 3 · (−2) + 4 · 1

–=

»−1 0−3 −2

DA =

»−1 −20 1

– »1 23 4

–=

»(−1) · 1 + (−2) · 3 (−1) · 2 + (−2) · 4

0 · 1 + 1 · 3 0 · 2 + 1 · 4

–=

»−7 −103 4

Jing Li (UofO) MAT 1332 C March 8, 2010 30 / 39

Page 298: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

A2 = AA =

»1 23 4

– »1 23 4

–=

»1 · 1 + 2 · 3 1 · 2 + 2 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

–=

»7 10

15 22

AD =

»1 23 4

– »−1 −20 1

–=

»1 · (−1) + 2 · 0 1 · (−2) + 2 · 13 · (−1) + 4 · 0 3 · (−2) + 4 · 1

=

»−1 0−3 −2

DA =

»−1 −20 1

– »1 23 4

–=

»(−1) · 1 + (−2) · 3 (−1) · 2 + (−2) · 4

0 · 1 + 1 · 3 0 · 2 + 1 · 4

–=

»−7 −103 4

Jing Li (UofO) MAT 1332 C March 8, 2010 30 / 39

Page 299: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

A2 = AA =

»1 23 4

– »1 23 4

–=

»1 · 1 + 2 · 3 1 · 2 + 2 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

–=

»7 10

15 22

AD =

»1 23 4

– »−1 −20 1

–=

»1 · (−1) + 2 · 0 1 · (−2) + 2 · 13 · (−1) + 4 · 0 3 · (−2) + 4 · 1

–=

»−1 0−3 −2

DA =

»−1 −20 1

– »1 23 4

–=

»(−1) · 1 + (−2) · 3 (−1) · 2 + (−2) · 4

0 · 1 + 1 · 3 0 · 2 + 1 · 4

–=

»−7 −103 4

Jing Li (UofO) MAT 1332 C March 8, 2010 30 / 39

Page 300: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

A2 = AA =

»1 23 4

– »1 23 4

–=

»1 · 1 + 2 · 3 1 · 2 + 2 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

–=

»7 10

15 22

AD =

»1 23 4

– »−1 −20 1

–=

»1 · (−1) + 2 · 0 1 · (−2) + 2 · 13 · (−1) + 4 · 0 3 · (−2) + 4 · 1

–=

»−1 0−3 −2

DA =

»−1 −20 1

– »1 23 4

–=

»(−1) · 1 + (−2) · 3 (−1) · 2 + (−2) · 4

0 · 1 + 1 · 3 0 · 2 + 1 · 4

–=

»−7 −103 4

Jing Li (UofO) MAT 1332 C March 8, 2010 30 / 39

Page 301: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

A2 = AA =

»1 23 4

– »1 23 4

–=

»1 · 1 + 2 · 3 1 · 2 + 2 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

–=

»7 10

15 22

AD =

»1 23 4

– »−1 −20 1

–=

»1 · (−1) + 2 · 0 1 · (−2) + 2 · 13 · (−1) + 4 · 0 3 · (−2) + 4 · 1

–=

»−1 0−3 −2

DA =

»−1 −20 1

– »1 23 4

–=

»(−1) · 1 + (−2) · 3 (−1) · 2 + (−2) · 4

0 · 1 + 1 · 3 0 · 2 + 1 · 4

=

»−7 −103 4

Jing Li (UofO) MAT 1332 C March 8, 2010 30 / 39

Page 302: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 4 cont’d

A2 = AA =

»1 23 4

– »1 23 4

–=

»1 · 1 + 2 · 3 1 · 2 + 2 · 43 · 1 + 4 · 3 3 · 2 + 4 · 4

–=

»7 10

15 22

AD =

»1 23 4

– »−1 −20 1

–=

»1 · (−1) + 2 · 0 1 · (−2) + 2 · 13 · (−1) + 4 · 0 3 · (−2) + 4 · 1

–=

»−1 0−3 −2

DA =

»−1 −20 1

– »1 23 4

–=

»(−1) · 1 + (−2) · 3 (−1) · 2 + (−2) · 4

0 · 1 + 1 · 3 0 · 2 + 1 · 4

–=

»−7 −103 4

Jing Li (UofO) MAT 1332 C March 8, 2010 30 / 39

Page 303: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

AD =

»−1 0−3 −2

–, DA =

»−7 −103 4

–,−→ AD 6= DA.

Note:

The order of the product matters!

Matrix multiplication is NOT commutative, even if both products are defined.

Jing Li (UofO) MAT 1332 C March 8, 2010 31 / 39

Page 304: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

AD =

»−1 0−3 −2

–, DA =

»−7 −103 4

–,−→ AD 6= DA.

Note:

The order of the product matters!

Matrix multiplication is NOT commutative, even if both products are defined.

Jing Li (UofO) MAT 1332 C March 8, 2010 31 / 39

Page 305: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

AD =

»−1 0−3 −2

–, DA =

»−7 −103 4

–,−→ AD 6= DA.

Note:

The order of the product matters!

Matrix multiplication is NOT commutative, even if both products are defined.

Jing Li (UofO) MAT 1332 C March 8, 2010 31 / 39

Page 306: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Multiplication of the identity matrix

For real number arithmetic, we know that 1 · a = a · 1 = a.

The identity matrix gives the same result in matrix multiplication

Multiplication of the identity matrix

If A is n ×m matrix, then we have

InA = AIm = A

Note: we really do need different identity matrices on each side of A that will dependupon the size of A.

Jing Li (UofO) MAT 1332 C March 8, 2010 32 / 39

Page 307: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Multiplication of the identity matrix

For real number arithmetic, we know that 1 · a = a · 1 = a.

The identity matrix gives the same result in matrix multiplication

Multiplication of the identity matrix

If A is n ×m matrix, then we have

InA = AIm = A

Note: we really do need different identity matrices on each side of A that will dependupon the size of A.

Jing Li (UofO) MAT 1332 C March 8, 2010 32 / 39

Page 308: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Multiplication of the identity matrix

For real number arithmetic, we know that 1 · a = a · 1 = a.

The identity matrix gives the same result in matrix multiplication

Multiplication of the identity matrix

If A is n ×m matrix, then we have

InA = AIm = A

Note: we really do need different identity matrices on each side of A that will dependupon the size of A.

Jing Li (UofO) MAT 1332 C March 8, 2010 32 / 39

Page 309: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Multiplication of the identity matrix

For real number arithmetic, we know that 1 · a = a · 1 = a.

The identity matrix gives the same result in matrix multiplication

Multiplication of the identity matrix

If A is n ×m matrix, then we have

InA = AIm = A

Note: we really do need different identity matrices on each side of A that will dependupon the size of A.

Jing Li (UofO) MAT 1332 C March 8, 2010 32 / 39

Page 310: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 5:

For

A =

24 10 0−3 8−1 11

35 ,

then

I3A =

24 1 0 00 1 00 0 1

35 24 10 0−3 8−1 11

35 =

24 10 0−3 8−1 11

35AI2 =

24 10 0−3 8−1 11

35 » 1 00 1

–=

24 10 0−3 8−1 11

35Hence,

I3A = AI2 = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 33 / 39

Page 311: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 5:

For

A =

24 10 0−3 8−1 11

35 ,

then

I3A =

24 1 0 00 1 00 0 1

35

24 10 0−3 8−1 11

35 =

24 10 0−3 8−1 11

35AI2 =

24 10 0−3 8−1 11

35 » 1 00 1

–=

24 10 0−3 8−1 11

35Hence,

I3A = AI2 = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 33 / 39

Page 312: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 5:

For

A =

24 10 0−3 8−1 11

35 ,

then

I3A =

24 1 0 00 1 00 0 1

35 24 10 0−3 8−1 11

35

=

24 10 0−3 8−1 11

35AI2 =

24 10 0−3 8−1 11

35 » 1 00 1

–=

24 10 0−3 8−1 11

35Hence,

I3A = AI2 = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 33 / 39

Page 313: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 5:

For

A =

24 10 0−3 8−1 11

35 ,

then

I3A =

24 1 0 00 1 00 0 1

35 24 10 0−3 8−1 11

35 =

24 10 0−3 8−1 11

35

AI2 =

24 10 0−3 8−1 11

35 » 1 00 1

–=

24 10 0−3 8−1 11

35Hence,

I3A = AI2 = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 33 / 39

Page 314: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 5:

For

A =

24 10 0−3 8−1 11

35 ,

then

I3A =

24 1 0 00 1 00 0 1

35 24 10 0−3 8−1 11

35 =

24 10 0−3 8−1 11

35AI2 =

24 10 0−3 8−1 11

35

»1 00 1

–=

24 10 0−3 8−1 11

35Hence,

I3A = AI2 = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 33 / 39

Page 315: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 5:

For

A =

24 10 0−3 8−1 11

35 ,

then

I3A =

24 1 0 00 1 00 0 1

35 24 10 0−3 8−1 11

35 =

24 10 0−3 8−1 11

35AI2 =

24 10 0−3 8−1 11

35 » 1 00 1

=

24 10 0−3 8−1 11

35Hence,

I3A = AI2 = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 33 / 39

Page 316: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 5:

For

A =

24 10 0−3 8−1 11

35 ,

then

I3A =

24 1 0 00 1 00 0 1

35 24 10 0−3 8−1 11

35 =

24 10 0−3 8−1 11

35AI2 =

24 10 0−3 8−1 11

35 » 1 00 1

–=

24 10 0−3 8−1 11

35

Hence,I3A = AI2 = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 33 / 39

Page 317: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 5:

For

A =

24 10 0−3 8−1 11

35 ,

then

I3A =

24 1 0 00 1 00 0 1

35 24 10 0−3 8−1 11

35 =

24 10 0−3 8−1 11

35AI2 =

24 10 0−3 8−1 11

35 » 1 00 1

–=

24 10 0−3 8−1 11

35Hence,

I3A = AI2 = A.

Jing Li (UofO) MAT 1332 C March 8, 2010 33 / 39

Page 318: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Operations of the zero matrix:

The zero matrix (denoted by 0 for a general matrix and 0 for a column/row vector) willtake the place of the number 0 in most of the matrix arithmetic.

Zero matrix properties: In the following properties, A is a matrix and 0 is the zeromatrix sized appropriately for the indicated operation to be valid.

1 A + 0 = 0 + A, (A is m × n, 0 is m × n)2 A− A = 0, (A is m × n, 0 is m × n)3 0− A = −A, (A is m × n, 0 is m × n)4 0A = 0, (A is m × n, the first 0 is k ×m, the second 0 is k × n)5 A0 = 0, (A is m × n, the first 0 is n × k , the second 0 is m × k )

Jing Li (UofO) MAT 1332 C March 8, 2010 34 / 39

Page 319: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Operations of the zero matrix:

The zero matrix (denoted by 0 for a general matrix and 0 for a column/row vector) willtake the place of the number 0 in most of the matrix arithmetic.Zero matrix properties: In the following properties, A is a matrix and 0 is the zeromatrix sized appropriately for the indicated operation to be valid.

1 A + 0 = 0 + A, (A is m × n, 0 is m × n)2 A− A = 0, (A is m × n, 0 is m × n)3 0− A = −A, (A is m × n, 0 is m × n)4 0A = 0, (A is m × n, the first 0 is k ×m, the second 0 is k × n)5 A0 = 0, (A is m × n, the first 0 is n × k , the second 0 is m × k )

Jing Li (UofO) MAT 1332 C March 8, 2010 34 / 39

Page 320: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Operations of the zero matrix:

Now, in real number arithmetic we know that

if ab = ac, and a 6= 0, then b = c. (cancellation law)

if ab = 0 then a = 0 and/or b = 0. (the zero factor property)

BUT, we see the following two examples:

Jing Li (UofO) MAT 1332 C March 8, 2010 35 / 39

Page 321: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Operations of the zero matrix:

Now, in real number arithmetic we know that

if ab = ac, and a 6= 0, then b = c. (cancellation law)

if ab = 0 then a = 0 and/or b = 0. (the zero factor property)

BUT, we see the following two examples:

Jing Li (UofO) MAT 1332 C March 8, 2010 35 / 39

Page 322: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 6:

Consider

A =

»−3 2−6 4

–, B =

»−1 23 −2

–, C =

»1 46 1

–,

calculate AB and AC:

AB =

»−3 2−6 4

– »−1 23 −2

–=

»−3 · (−1) + 2 · 3 −3 · 2 + 2 · (−2)−6 · (−1) + 4 · 3 −6 · 2 + 4 · (−2)

–=

»9 −10

18 −20

AC =

»−3 2−6 4

– »1 46 1

–=

»−3 · 1 + 2 · 6 −3 · 4 + 2 · 1−6 · 1 + 4 · 6 −6 · 4 + 4 · 1

–=

»9 −1018 −20

–Note:

AB = AC , (A 6= 0, B 6= C). So from AB = AC we can’t have B = C.The cancellation law will not be valid in general for matrix multiplication.

Jing Li (UofO) MAT 1332 C March 8, 2010 36 / 39

Page 323: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 6:

Consider

A =

»−3 2−6 4

–, B =

»−1 23 −2

–, C =

»1 46 1

–,

calculate AB and AC:

AB =

»−3 2−6 4

– »−1 23 −2

=

»−3 · (−1) + 2 · 3 −3 · 2 + 2 · (−2)−6 · (−1) + 4 · 3 −6 · 2 + 4 · (−2)

–=

»9 −10

18 −20

AC =

»−3 2−6 4

– »1 46 1

–=

»−3 · 1 + 2 · 6 −3 · 4 + 2 · 1−6 · 1 + 4 · 6 −6 · 4 + 4 · 1

–=

»9 −1018 −20

–Note:

AB = AC , (A 6= 0, B 6= C). So from AB = AC we can’t have B = C.The cancellation law will not be valid in general for matrix multiplication.

Jing Li (UofO) MAT 1332 C March 8, 2010 36 / 39

Page 324: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 6:

Consider

A =

»−3 2−6 4

–, B =

»−1 23 −2

–, C =

»1 46 1

–,

calculate AB and AC:

AB =

»−3 2−6 4

– »−1 23 −2

–=

»−3 · (−1) + 2 · 3 −3 · 2 + 2 · (−2)−6 · (−1) + 4 · 3 −6 · 2 + 4 · (−2)

=

»9 −10

18 −20

AC =

»−3 2−6 4

– »1 46 1

–=

»−3 · 1 + 2 · 6 −3 · 4 + 2 · 1−6 · 1 + 4 · 6 −6 · 4 + 4 · 1

–=

»9 −1018 −20

–Note:

AB = AC , (A 6= 0, B 6= C). So from AB = AC we can’t have B = C.The cancellation law will not be valid in general for matrix multiplication.

Jing Li (UofO) MAT 1332 C March 8, 2010 36 / 39

Page 325: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 6:

Consider

A =

»−3 2−6 4

–, B =

»−1 23 −2

–, C =

»1 46 1

–,

calculate AB and AC:

AB =

»−3 2−6 4

– »−1 23 −2

–=

»−3 · (−1) + 2 · 3 −3 · 2 + 2 · (−2)−6 · (−1) + 4 · 3 −6 · 2 + 4 · (−2)

–=

»9 −10

18 −20

AC =

»−3 2−6 4

– »1 46 1

–=

»−3 · 1 + 2 · 6 −3 · 4 + 2 · 1−6 · 1 + 4 · 6 −6 · 4 + 4 · 1

–=

»9 −1018 −20

–Note:

AB = AC , (A 6= 0, B 6= C). So from AB = AC we can’t have B = C.The cancellation law will not be valid in general for matrix multiplication.

Jing Li (UofO) MAT 1332 C March 8, 2010 36 / 39

Page 326: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 6:

Consider

A =

»−3 2−6 4

–, B =

»−1 23 −2

–, C =

»1 46 1

–,

calculate AB and AC:

AB =

»−3 2−6 4

– »−1 23 −2

–=

»−3 · (−1) + 2 · 3 −3 · 2 + 2 · (−2)−6 · (−1) + 4 · 3 −6 · 2 + 4 · (−2)

–=

»9 −10

18 −20

AC =

»−3 2−6 4

– »1 46 1

=

»−3 · 1 + 2 · 6 −3 · 4 + 2 · 1−6 · 1 + 4 · 6 −6 · 4 + 4 · 1

–=

»9 −1018 −20

–Note:

AB = AC , (A 6= 0, B 6= C). So from AB = AC we can’t have B = C.The cancellation law will not be valid in general for matrix multiplication.

Jing Li (UofO) MAT 1332 C March 8, 2010 36 / 39

Page 327: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 6:

Consider

A =

»−3 2−6 4

–, B =

»−1 23 −2

–, C =

»1 46 1

–,

calculate AB and AC:

AB =

»−3 2−6 4

– »−1 23 −2

–=

»−3 · (−1) + 2 · 3 −3 · 2 + 2 · (−2)−6 · (−1) + 4 · 3 −6 · 2 + 4 · (−2)

–=

»9 −10

18 −20

AC =

»−3 2−6 4

– »1 46 1

–=

»−3 · 1 + 2 · 6 −3 · 4 + 2 · 1−6 · 1 + 4 · 6 −6 · 4 + 4 · 1

=

»9 −1018 −20

–Note:

AB = AC , (A 6= 0, B 6= C). So from AB = AC we can’t have B = C.The cancellation law will not be valid in general for matrix multiplication.

Jing Li (UofO) MAT 1332 C March 8, 2010 36 / 39

Page 328: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 6:

Consider

A =

»−3 2−6 4

–, B =

»−1 23 −2

–, C =

»1 46 1

–,

calculate AB and AC:

AB =

»−3 2−6 4

– »−1 23 −2

–=

»−3 · (−1) + 2 · 3 −3 · 2 + 2 · (−2)−6 · (−1) + 4 · 3 −6 · 2 + 4 · (−2)

–=

»9 −10

18 −20

AC =

»−3 2−6 4

– »1 46 1

–=

»−3 · 1 + 2 · 6 −3 · 4 + 2 · 1−6 · 1 + 4 · 6 −6 · 4 + 4 · 1

–=

»9 −1018 −20

Note:AB = AC , (A 6= 0, B 6= C). So from AB = AC we can’t have B = C.The cancellation law will not be valid in general for matrix multiplication.

Jing Li (UofO) MAT 1332 C March 8, 2010 36 / 39

Page 329: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 6:

Consider

A =

»−3 2−6 4

–, B =

»−1 23 −2

–, C =

»1 46 1

–,

calculate AB and AC:

AB =

»−3 2−6 4

– »−1 23 −2

–=

»−3 · (−1) + 2 · 3 −3 · 2 + 2 · (−2)−6 · (−1) + 4 · 3 −6 · 2 + 4 · (−2)

–=

»9 −10

18 −20

AC =

»−3 2−6 4

– »1 46 1

–=

»−3 · 1 + 2 · 6 −3 · 4 + 2 · 1−6 · 1 + 4 · 6 −6 · 4 + 4 · 1

–=

»9 −1018 −20

–Note:

AB = AC , (A 6= 0, B 6= C).

So from AB = AC we can’t have B = C.The cancellation law will not be valid in general for matrix multiplication.

Jing Li (UofO) MAT 1332 C March 8, 2010 36 / 39

Page 330: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 6:

Consider

A =

»−3 2−6 4

–, B =

»−1 23 −2

–, C =

»1 46 1

–,

calculate AB and AC:

AB =

»−3 2−6 4

– »−1 23 −2

–=

»−3 · (−1) + 2 · 3 −3 · 2 + 2 · (−2)−6 · (−1) + 4 · 3 −6 · 2 + 4 · (−2)

–=

»9 −10

18 −20

AC =

»−3 2−6 4

– »1 46 1

–=

»−3 · 1 + 2 · 6 −3 · 4 + 2 · 1−6 · 1 + 4 · 6 −6 · 4 + 4 · 1

–=

»9 −1018 −20

–Note:

AB = AC , (A 6= 0, B 6= C). So from AB = AC we can’t have B = C.

The cancellation law will not be valid in general for matrix multiplication.

Jing Li (UofO) MAT 1332 C March 8, 2010 36 / 39

Page 331: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 6:

Consider

A =

»−3 2−6 4

–, B =

»−1 23 −2

–, C =

»1 46 1

–,

calculate AB and AC:

AB =

»−3 2−6 4

– »−1 23 −2

–=

»−3 · (−1) + 2 · 3 −3 · 2 + 2 · (−2)−6 · (−1) + 4 · 3 −6 · 2 + 4 · (−2)

–=

»9 −10

18 −20

AC =

»−3 2−6 4

– »1 46 1

–=

»−3 · 1 + 2 · 6 −3 · 4 + 2 · 1−6 · 1 + 4 · 6 −6 · 4 + 4 · 1

–=

»9 −1018 −20

–Note:

AB = AC , (A 6= 0, B 6= C). So from AB = AC we can’t have B = C.The cancellation law will not be valid in general for matrix multiplication.

Jing Li (UofO) MAT 1332 C March 8, 2010 36 / 39

Page 332: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 7: (also see the last example in the additional lecture notes)

Consider

A =

»1 22 4

–, B =

»−16 2

8 −1

–,

Then

AB =

»1 22 4

– »−16 2

8 −1

=

»1 · (−16) + 2 · 8 1 · 2 + 2 · (−1)2 · (−16) + 4 · 8 2 · 2 + 4 · (−1)

–=

»0 00 0

–Note:

AB = 0 despite the face that A 6= 0, B 6= 0. In this case, the zero factor propertydose NOT hold.

There will be no zero factor property for the multiplication of any two randommatrices.

Jing Li (UofO) MAT 1332 C March 8, 2010 37 / 39

Page 333: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 7: (also see the last example in the additional lecture notes)

Consider

A =

»1 22 4

–, B =

»−16 2

8 −1

–,

Then

AB =

»1 22 4

– »−16 2

8 −1

–=

»1 · (−16) + 2 · 8 1 · 2 + 2 · (−1)2 · (−16) + 4 · 8 2 · 2 + 4 · (−1)

=

»0 00 0

–Note:

AB = 0 despite the face that A 6= 0, B 6= 0. In this case, the zero factor propertydose NOT hold.

There will be no zero factor property for the multiplication of any two randommatrices.

Jing Li (UofO) MAT 1332 C March 8, 2010 37 / 39

Page 334: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 7: (also see the last example in the additional lecture notes)

Consider

A =

»1 22 4

–, B =

»−16 2

8 −1

–,

Then

AB =

»1 22 4

– »−16 2

8 −1

–=

»1 · (−16) + 2 · 8 1 · 2 + 2 · (−1)2 · (−16) + 4 · 8 2 · 2 + 4 · (−1)

–=

»0 00 0

Note:

AB = 0 despite the face that A 6= 0, B 6= 0. In this case, the zero factor propertydose NOT hold.

There will be no zero factor property for the multiplication of any two randommatrices.

Jing Li (UofO) MAT 1332 C March 8, 2010 37 / 39

Page 335: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 7: (also see the last example in the additional lecture notes)

Consider

A =

»1 22 4

–, B =

»−16 2

8 −1

–,

Then

AB =

»1 22 4

– »−16 2

8 −1

–=

»1 · (−16) + 2 · 8 1 · 2 + 2 · (−1)2 · (−16) + 4 · 8 2 · 2 + 4 · (−1)

–=

»0 00 0

–Note:

AB = 0 despite the face that A 6= 0, B 6= 0.

In this case, the zero factor propertydose NOT hold.

There will be no zero factor property for the multiplication of any two randommatrices.

Jing Li (UofO) MAT 1332 C March 8, 2010 37 / 39

Page 336: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 7: (also see the last example in the additional lecture notes)

Consider

A =

»1 22 4

–, B =

»−16 2

8 −1

–,

Then

AB =

»1 22 4

– »−16 2

8 −1

–=

»1 · (−16) + 2 · 8 1 · 2 + 2 · (−1)2 · (−16) + 4 · 8 2 · 2 + 4 · (−1)

–=

»0 00 0

–Note:

AB = 0 despite the face that A 6= 0, B 6= 0. In this case, the zero factor propertydose NOT hold.

There will be no zero factor property for the multiplication of any two randommatrices.

Jing Li (UofO) MAT 1332 C March 8, 2010 37 / 39

Page 337: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 7: (also see the last example in the additional lecture notes)

Consider

A =

»1 22 4

–, B =

»−16 2

8 −1

–,

Then

AB =

»1 22 4

– »−16 2

8 −1

–=

»1 · (−16) + 2 · 8 1 · 2 + 2 · (−1)2 · (−16) + 4 · 8 2 · 2 + 4 · (−1)

–=

»0 00 0

–Note:

AB = 0 despite the face that A 6= 0, B 6= 0. In this case, the zero factor propertydose NOT hold.

There will be no zero factor property for the multiplication of any two randommatrices.

Jing Li (UofO) MAT 1332 C March 8, 2010 37 / 39

Page 338: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 8:

Consider A =

24 1−12

35 , B =ˆ

1 3 2˜, then AB =?.

Solutions:

A is 3× 1, and B is 1× 3. Then AB should be 3× 3.

AB =

24 1−12

35 ˆ 1 3 2˜

=

24 1 · 1 1 · 3 1 · 2−1 · 1 −1 · 3 −1 · 22 · 1 2 · 3 2 · 2

35=

24 1 3 2−1 −3 −22 6 4

35

Jing Li (UofO) MAT 1332 C March 8, 2010 38 / 39

Page 339: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 8:

Consider A =

24 1−12

35 , B =ˆ

1 3 2˜, then AB =?.

Solutions: A is 3× 1, and B is 1× 3.

Then AB should be 3× 3.

AB =

24 1−12

35 ˆ 1 3 2˜

=

24 1 · 1 1 · 3 1 · 2−1 · 1 −1 · 3 −1 · 22 · 1 2 · 3 2 · 2

35=

24 1 3 2−1 −3 −22 6 4

35

Jing Li (UofO) MAT 1332 C March 8, 2010 38 / 39

Page 340: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 8:

Consider A =

24 1−12

35 , B =ˆ

1 3 2˜, then AB =?.

Solutions: A is 3× 1, and B is 1× 3. Then AB should be 3× 3.

AB =

24 1−12

35 ˆ 1 3 2˜

=

24 1 · 1 1 · 3 1 · 2−1 · 1 −1 · 3 −1 · 22 · 1 2 · 3 2 · 2

35=

24 1 3 2−1 −3 −22 6 4

35

Jing Li (UofO) MAT 1332 C March 8, 2010 38 / 39

Page 341: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 8:

Consider A =

24 1−12

35 , B =ˆ

1 3 2˜, then AB =?.

Solutions: A is 3× 1, and B is 1× 3. Then AB should be 3× 3.

AB =

24 1−12

35 ˆ 1 3 2˜

=

24 1 · 1 1 · 3 1 · 2−1 · 1 −1 · 3 −1 · 22 · 1 2 · 3 2 · 2

35=

24 1 3 2−1 −3 −22 6 4

35

Jing Li (UofO) MAT 1332 C March 8, 2010 38 / 39

Page 342: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 8:

Consider A =

24 1−12

35 , B =ˆ

1 3 2˜, then AB =?.

Solutions: A is 3× 1, and B is 1× 3. Then AB should be 3× 3.

AB =

24 1−12

35 ˆ 1 3 2˜

=

24 1 · 1 1 · 3 1 · 2−1 · 1 −1 · 3 −1 · 22 · 1 2 · 3 2 · 2

35

=

24 1 3 2−1 −3 −22 6 4

35

Jing Li (UofO) MAT 1332 C March 8, 2010 38 / 39

Page 343: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Example 8:

Consider A =

24 1−12

35 , B =ˆ

1 3 2˜, then AB =?.

Solutions: A is 3× 1, and B is 1× 3. Then AB should be 3× 3.

AB =

24 1−12

35 ˆ 1 3 2˜

=

24 1 · 1 1 · 3 1 · 2−1 · 1 −1 · 3 −1 · 22 · 1 2 · 3 2 · 2

35=

24 1 3 2−1 −3 −22 6 4

35

Jing Li (UofO) MAT 1332 C March 8, 2010 38 / 39

Page 344: Calculus for Life Sciences · 2013-01-22 · Calculus for Life Sciences MAT 1332 C Winter 2010 Jing Li Department of Mathematics and Statistics University of Ottawa March 8, 2010

Linear Algebra II: Vectors and Matrices Operations

Thank you!I will greatly appreciate it if you can tell me the typo that you find.

Jing Li (UofO) MAT 1332 C March 8, 2010 39 / 39