Abstraction and Abstract Thinking

Part 1 “Algorithms”

Part 2 “Abstract Networks”

Recap

Statement of The Problem

Theory of the Problem

Modelling of the Problem

Algorithmic description

Programming the Algorithm

Executing the Program

done

done

today

Future delights

The ability to separate the high level view of an entity or an operation from the low-level details of its implementation.

Abstraction

AbstractProcess / (Data)

Non-AbstractProcess / (Data)

Take the first three digits of your phone number and multiply by 80

Add 1 to the result

Now Multiply by 250 and add the last 4 digits of your phone number

Add the last 4 digits of your phone number again

Subtract 250 and divide by 2. What do you see?

A Fun Algorithm

A Serious Algorithm – Sum of a List

Get the list of numbersSet sum to zero

Move through the list until it is ended

get the next number in the list add it to the sum

Output the sum

The Gale-Shapley algorithm involves a number of iterations.

Each unengaged man proposes to the preferred woman to whom he has not yet proposed.

Each woman then considers all her suitors and tells the one she most prefers "Maybe" and all the rest of them "No".

She is then provisionally "engaged".

In each subsequent round, each unengaged man proposes to one woman to whom he has not yet proposed.

The women once again replies with one "maybe" and rejects the rest.

A fun Algorithm - Stable Marriage

Stable Marriage - Example

A W

B X

C Y

D Z

A W

B X

C Y

D Z

A W

B X

C Y

D Z

A W

B X

C Y

D Z

A W

B X

C Y

D Z

A W

B X

C Y

D Z

A W

B X

C Y

D Z

A W

B X

C Y

D Z

A W

B X

C Y

D Z

An algorithm is a well-ordered collection of unambiguous and effectively computable operations that, when executed, produces a result, and halts in a finite amount of time. Schneider and Gersting (2004).

Definition of an Algorithm

Algorithms are concepts which exist outside programming languages. They are abstract method for computing something, whereas a program is an embodiment of this method. Donald Knuth (1966)

Fun -3- Magic Card Trick

Consider this example taken from the instructions on the back of a shampoo bottle:

• Wet hair• Lather• Rinse• Repeat

Is this an Algorithm?

AlgorithmicProcess

Non-AlgorithmicProcess

Examples of Recent Serious Algorithms

(1) Matching of kidney donor – recipients

(2) Traffic-flow engineering

(3) Cancer Research.

Examples of “Standard” Computing Algorithms

(1a) Sequential Search

(1b) Binary Search

(2a) Selection Sort

(2c) Quicksort

DanielBob GrantAnne Carol Nathan Sue

Sequential Search Algorithm - Description

DanielBob GrantAnne Carol Nathan Sue

Sequential Search Algorithm - Example

Binary Search Algorithm - Description

1. Get the list of names 1,2,3,…N

2. Set “begin” to 1 and “end” to N

3. Set “found” to no

4. While “found” is no

1. Set “m” to middle value between “begin” and “end”

2. If “name” is “asked name”

1. Set “found” to yes

3. Else if “name” precedes “asked name” set “end” = m - 1

4. Else set “begin” to m + 1

DanielBob GrantAnne Carol Nathan Sue

Binary Search Algorithm - Example

How to Make a Binary Tree

Daniel

Bob Nathan

Anne Carol Grant Sue

Binary Search (Tree Representation)

Depth of a Binary Tree (complete)

1 2 34 5 67 8 9 10 11 12 13 14 15

4

2

1 3

6

5 7

Depth of a Binary Tree (incomplete)

1 2 3 4 5 6 7 8

4

2

1 3

6

5 7

8

0

10

20

30

40

50

60

0 10 20 30 40 50 60

Series1

Series2

Comparison of Sequential and Binary Search

Selection Sort Algorithm - Description

1. Get the list of numbers

2. Put the wall at the beginning

3. While there are more elements in the unsorted part

1. Find the smallest element

2. Swap with the first element in the unsorted part

3. Move the wall one element to the right

10 30 20 5 18 25

Selection Sort Algorithm - Example

Quick Sort Algorithm - Description

1. Get the list

2. Choose a “pivot” from the list

3. Move all elements less than the pivot to the left of the pivot and the greater elements to the right of the pivot.

4. Recursively apply 2,3 to the sub-lists generated

13 81 92 65 43 31 57 26 75 0

Quicksort Algorithm Example

0

50000

100000

150000

200000

250000

0 50000 100000 150000 200000 250000

Series1

Series2

Comparison of Selection sort and Quicksort

Hamiltonian Cycles

Problem: Find a path between n cities to

(i) Visit each city once

(ii) End up at the starting city.

A B

C D

Hamiltonian Cycles

Application of the TSP is to logistics. Good routes or schedules for:

• trucks (Dantzig & Ramser, 1959)• order-pickers in a warehouse (Ratli & Rosenthal, 1981)• service engineers (Pante, Lowe & Chandrasekaran, 1987)• aircraft (Boland, Jones & Nemhauser, 1994)• tourists (Gentili, 2003)

Travelling Salesman Problem

Find the Hamiltonian circuit between a number of cities where each link has an associated cost

TSP – Simulated Annealing

1 pick an initial solution

2 set an initial temperature

3 choose the next neighbour of the current solution:

4 if the neighbour is “better” make that neighbour the current solution

5 if the neighbour is “worse”, make this neighbour the current solution, based on the temperature and how “worse” the neighbour is. (Probabilitistic calculation).

6 decrease the temperature slightly

7 go to 3.

TSP – Ant Colony Model

Pattern Matching

C C G A T A C G T T A G C T T A C

Pattern Matching (Worst Case -1-)

C C C C C C C C C

Pattern Matching (Worst Case -2-)

A A A A A A A A A

Pattern Matching (Best Case)

A B C D E F G H I

Worst-Case Execution Time of Algorithms

Sequential Search

Binary Search

Selection Sort

Quicksort

Pattern Matching

Permutation

2

( )

(log )

( )

( log )

( )

(2 )n

n

n

n

n n

mn

Worst-Case Execution Time on a 2GHz Pentium

2

( )

(log )

( )

( log )

( )

(2 )n

n

n

n

n n

mn

10 20 100 1000n

Programming Problems

Uncomputable Computable

Time Space

Tractable Intractable

Classification of Algorithms

A.N.Whitehead and Leibnitz

“It is a profoundly erroneous truism, repeated by all copy books and by eminent people when they are making speeches, that we should cultivate the habit of thinking of what we are doing.

The precise opposite is the case. Civilization advances by extending the number of important operations which we can perform without thinking”.

An Introduction to Mathematics (1911).

“It is unworthy of excellent men to lose hours like slaves in the calculation which could safely be relegated to anyone else if machines were used”