Mathematical Logic Lecture 1: Introduction and background
Transcript of Mathematical Logic Lecture 1: Introduction and background
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Mathematical Logic
Lecture 1: Introduction and background
Ashutosh Gupta
TIFR, India
Compile date: 2015-08-10
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Section 1.1
What is logic?
![Page 3: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/3.jpg)
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What is logic?
I Have you ever said to someone “be logical”?
I whatever your intuition was that is logic
I Mathematization/Formalization of the intuition is mathematical logic
I Two streams of studying logicI use of logic : logic as a tool to study something elseI properties of logic: since logic is itself has mathematical structure, we may
study its mathematical properties
Self reference will haunt us!!
![Page 4: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/4.jpg)
3
What is logic?
I Have you ever said to someone “be logical”?I whatever your intuition was that is logic
I Mathematization/Formalization of the intuition is mathematical logic
I Two streams of studying logicI use of logic : logic as a tool to study something elseI properties of logic: since logic is itself has mathematical structure, we may
study its mathematical properties
Self reference will haunt us!!
![Page 5: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/5.jpg)
3
What is logic?
I Have you ever said to someone “be logical”?I whatever your intuition was that is logic
I Mathematization/Formalization of the intuition is mathematical logic
I Two streams of studying logicI use of logic : logic as a tool to study something elseI properties of logic: since logic is itself has mathematical structure, we may
study its mathematical properties
Self reference will haunt us!!
![Page 6: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/6.jpg)
3
What is logic?
I Have you ever said to someone “be logical”?I whatever your intuition was that is logic
I Mathematization/Formalization of the intuition is mathematical logic
I Two streams of studying logicI use of logic : logic as a tool to study something else
I properties of logic: since logic is itself has mathematical structure, we maystudy its mathematical properties
Self reference will haunt us!!
![Page 7: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/7.jpg)
3
What is logic?
I Have you ever said to someone “be logical”?I whatever your intuition was that is logic
I Mathematization/Formalization of the intuition is mathematical logic
I Two streams of studying logicI use of logic : logic as a tool to study something elseI properties of logic: since logic is itself has mathematical structure, we may
study its mathematical properties
Self reference will haunt us!!
![Page 8: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/8.jpg)
3
What is logic?
I Have you ever said to someone “be logical”?I whatever your intuition was that is logic
I Mathematization/Formalization of the intuition is mathematical logic
I Two streams of studying logicI use of logic : logic as a tool to study something elseI properties of logic: since logic is itself has mathematical structure, we may
study its mathematical properties
Self reference will haunt us!!
![Page 9: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/9.jpg)
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Why study logic?
Differential equationsare the calculus of
Electrical engineering
Logicis the calculus ofComputer science
Logic provides tools to define/manipulate computational objects
![Page 10: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/10.jpg)
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Why study logic?
Differential equationsare the calculus of
Electrical engineering
Logicis the calculus ofComputer science
Logic provides tools to define/manipulate computational objects
![Page 11: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/11.jpg)
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Why study logic?
Differential equationsare the calculus of
Electrical engineering
Logicis the calculus ofComputer science
Logic provides tools to define/manipulate computational objects
![Page 12: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/12.jpg)
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Defining logic
Logic is about inferring conclusions from given premises
Example 1.1
1. Humans are mortal
2. Socrates is a human
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Socrates is mortal
Intuitive Pattern:
1. αs are β
2. γ is an α
γ is βwhere α and γ are noun andβ is adjective
1. Apostles are twelve
2. Peter is an apostle
7
Peter is twelveWhat wentwrong here?
Very easy to ill-define.Logic needs rigorous definitions!!
![Page 13: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/13.jpg)
5
Defining logic
Logic is about inferring conclusions from given premises
Example 1.1
1. Humans are mortal
2. Socrates is a human
3
Socrates is mortal
Intuitive Pattern:
1. αs are β
2. γ is an α
γ is βwhere α and γ are noun andβ is adjective
1. Apostles are twelve
2. Peter is an apostle
7
Peter is twelveWhat wentwrong here?
Very easy to ill-define.Logic needs rigorous definitions!!
![Page 14: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/14.jpg)
5
Defining logic
Logic is about inferring conclusions from given premises
Example 1.1
1. Humans are mortal
2. Socrates is a human
3
Socrates is mortal
Intuitive Pattern:
1. αs are β
2. γ is an α
γ is βwhere α and γ are noun andβ is adjective
1. Apostles are twelve
2. Peter is an apostle
7
Peter is twelveWhat wentwrong here?
Very easy to ill-define.Logic needs rigorous definitions!!
![Page 15: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/15.jpg)
5
Defining logic
Logic is about inferring conclusions from given premises
Example 1.1
1. Humans are mortal
2. Socrates is a human
3
Socrates is mortal
Intuitive Pattern:
1. αs are β
2. γ is an α
γ is βwhere α and γ are noun andβ is adjective
1. Apostles are twelve
2. Peter is an apostle
7
Peter is twelve
What wentwrong here?
Very easy to ill-define.Logic needs rigorous definitions!!
![Page 16: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/16.jpg)
5
Defining logic
Logic is about inferring conclusions from given premises
Example 1.1
1. Humans are mortal
2. Socrates is a human3
Socrates is mortal
Intuitive Pattern:
1. αs are β
2. γ is an α
γ is βwhere α and γ are noun andβ is adjective
1. Apostles are twelve
2. Peter is an apostle7
Peter is twelveWhat wentwrong here?
Very easy to ill-define.Logic needs rigorous definitions!!
![Page 17: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/17.jpg)
5
Defining logic
Logic is about inferring conclusions from given premises
Example 1.1
1. Humans are mortal
2. Socrates is a human3
Socrates is mortal
Intuitive Pattern:
1. αs are β
2. γ is an α
γ is βwhere α and γ are noun andβ is adjective
1. Apostles are twelve
2. Peter is an apostle7
Peter is twelveWhat wentwrong here?
Very easy to ill-define.Logic needs rigorous definitions!!
![Page 18: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/18.jpg)
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Section 1.2
Course contents
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The course
We will study the following topics
I Propositional logic
I First order logic
I Logical theories
I Incompleteness results
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Propositional logic (PL)Propositional logic
I deals with propositions,I only infers from the structure over propositions, andI does not look inside propositions.
Example 1.2
Is the following argument valid?If the seed catalog is correct then if seeds are planted in April then theflowers bloom in July. The flowers do not bloom in July. Therefore, if seedsare planted in April then the seed catalog is not correct.
Let us symbolize our problemIf c then if s then f . not f . Therefore, if s then not c.
I c = the seed catalogue is correct
I s = seeds are planted in April
I f = the flowers bloom in July
PL reasons over propositionalsymbols and logical connectives
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Propositional logic (PL)Propositional logic
I deals with propositions,I only infers from the structure over propositions, andI does not look inside propositions.
Example 1.2
Is the following argument valid?If the seed catalog is correct then if seeds are planted in April then theflowers bloom in July. The flowers do not bloom in July. Therefore, if seedsare planted in April then the seed catalog is not correct.
Let us symbolize our problemIf c then if s then f . not f . Therefore, if s then not c.
I c = the seed catalogue is correct
I s = seeds are planted in April
I f = the flowers bloom in July
PL reasons over propositionalsymbols and logical connectives
![Page 22: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/22.jpg)
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Propositional logic (PL)Propositional logic
I deals with propositions,I only infers from the structure over propositions, andI does not look inside propositions.
Example 1.2
Is the following argument valid?If the seed catalog is correct then if seeds are planted in April then theflowers bloom in July. The flowers do not bloom in July. Therefore, if seedsare planted in April then the seed catalog is not correct.
Let us symbolize our problemIf c then if s then f . not f . Therefore, if s then not c.
I c = the seed catalogue is correct
I s = seeds are planted in April
I f = the flowers bloom in July
PL reasons over propositionalsymbols and logical connectives
![Page 23: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/23.jpg)
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Propositional logic (PL)Propositional logic
I deals with propositions,I only infers from the structure over propositions, andI does not look inside propositions.
Example 1.2
Is the following argument valid?If the seed catalog is correct then if seeds are planted in April then theflowers bloom in July. The flowers do not bloom in July. Therefore, if seedsare planted in April then the seed catalog is not correct.
Let us symbolize our problemIf c then if s then f . not f . Therefore, if s then not c.
I c = the seed catalogue is correct
I s = seeds are planted in April
I f = the flowers bloom in July
PL reasons over propositionalsymbols and logical connectives
![Page 24: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/24.jpg)
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Propositional logic (PL)Propositional logic
I deals with propositions,I only infers from the structure over propositions, andI does not look inside propositions.
Example 1.2
Is the following argument valid?If the seed catalog is correct then if seeds are planted in April then theflowers bloom in July. The flowers do not bloom in July. Therefore, if seedsare planted in April then the seed catalog is not correct.
Let us symbolize our problemIf c then if s then f . not f . Therefore, if s then not c.
I c = the seed catalogue is correct
I s = seeds are planted in April
I f = the flowers bloom in July
PL reasons over propositionalsymbols and logical connectives
![Page 25: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/25.jpg)
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PL topics
We will study
I definition of PL
I proof systems for PL
I Normal forms
I Soundness, completeness, and other results
I PL solvers called, SAT solvers
I some other stuff..
![Page 26: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/26.jpg)
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First order logic (FOL)
First order logic
I looks inside the propositions,
I much more expressive,
I deals with quantifiers, parameterized propositions, and quantifiers, and
I can express lots of interesting math.
Example 1.3
Is the following argument valid?Humans are mortal. Socrates is a human. Therefore, Socrates is mortal.
In symbolic form,For all x if H(x) then M(x). H(s). Therefore, M(s).
I H(x) = x is a human
I M(x) = x is mortal
I s = Socrates FOL is not the most general logic.Many arguments can not be expressed in FOL
![Page 27: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/27.jpg)
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First order logic (FOL)
First order logic
I looks inside the propositions,
I much more expressive,
I deals with quantifiers, parameterized propositions, and quantifiers, and
I can express lots of interesting math.
Example 1.3
Is the following argument valid?Humans are mortal. Socrates is a human. Therefore, Socrates is mortal.
In symbolic form,For all x if H(x) then M(x). H(s). Therefore, M(s).
I H(x) = x is a human
I M(x) = x is mortal
I s = Socrates FOL is not the most general logic.Many arguments can not be expressed in FOL
![Page 28: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/28.jpg)
10
First order logic (FOL)
First order logic
I looks inside the propositions,
I much more expressive,
I deals with quantifiers, parameterized propositions, and quantifiers, and
I can express lots of interesting math.
Example 1.3
Is the following argument valid?Humans are mortal. Socrates is a human. Therefore, Socrates is mortal.
In symbolic form,For all x if H(x) then M(x). H(s). Therefore, M(s).
I H(x) = x is a human
I M(x) = x is mortal
I s = Socrates
FOL is not the most general logic.Many arguments can not be expressed in FOL
![Page 29: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/29.jpg)
10
First order logic (FOL)
First order logic
I looks inside the propositions,
I much more expressive,
I deals with quantifiers, parameterized propositions, and quantifiers, and
I can express lots of interesting math.
Example 1.3
Is the following argument valid?Humans are mortal. Socrates is a human. Therefore, Socrates is mortal.
In symbolic form,For all x if H(x) then M(x). H(s). Therefore, M(s).
I H(x) = x is a human
I M(x) = x is mortal
I s = Socrates FOL is not the most general logic.Many arguments can not be expressed in FOL
![Page 30: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/30.jpg)
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FOL topics
We will study
I definition of FOL
I proof systems for FOL
I Normal forms
I Soundness, completeness, and other results
I First order theorem provers
I some other stuff..
![Page 31: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/31.jpg)
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Logical theories
In a theory, we study validity of FOL arguments under some specializedassumptions (called axioms).
Example 1.4
Number theory uses symbols 0, 1, . . . , <, +,· with some special meaning(usually defined by some axioms)
The following sentence makes little sense until we assign the specialmeanings to > and ·.
∀x∃p.(p > x ∧ (∀v1. (v1 > 1⇒ ∀v2. p 6= v1 · v2)))
Under the meanings it says that there are arbitrarily large prime numbers.
In the previous example, we had a vague understanding of predicatex is human (H(x)). Here we precisely know what is predicate x < y .
![Page 32: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/32.jpg)
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Logical theories
In a theory, we study validity of FOL arguments under some specializedassumptions (called axioms).
Example 1.4
Number theory uses symbols 0, 1, . . . , <, +,· with some special meaning(usually defined by some axioms)
The following sentence makes little sense until we assign the specialmeanings to > and ·.
∀x∃p.(p > x ∧ (∀v1. (v1 > 1⇒ ∀v2. p 6= v1 · v2)))
Under the meanings it says that there are arbitrarily large prime numbers.
In the previous example, we had a vague understanding of predicatex is human (H(x)). Here we precisely know what is predicate x < y .
![Page 33: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/33.jpg)
12
Logical theories
In a theory, we study validity of FOL arguments under some specializedassumptions (called axioms).
Example 1.4
Number theory uses symbols 0, 1, . . . , <, +,· with some special meaning(usually defined by some axioms)
The following sentence makes little sense until we assign the specialmeanings to > and ·.
∀x∃p.(p > x ∧ (∀v1. (v1 > 1⇒ ∀v2. p 6= v1 · v2)))
Under the meanings it says that there are arbitrarily large prime numbers.
In the previous example, we had a vague understanding of predicatex is human (H(x)). Here we precisely know what is predicate x < y .
![Page 34: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/34.jpg)
12
Logical theories
In a theory, we study validity of FOL arguments under some specializedassumptions (called axioms).
Example 1.4
Number theory uses symbols 0, 1, . . . , <, +,· with some special meaning(usually defined by some axioms)
The following sentence makes little sense until we assign the specialmeanings to > and ·.
∀x∃p.(p > x ∧ (∀v1. (v1 > 1⇒ ∀v2. p 6= v1 · v2)))
Under the meanings it says that there are arbitrarily large prime numbers.
In the previous example, we had a vague understanding of predicatex is human (H(x)). Here we precisely know what is predicate x < y .
![Page 35: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/35.jpg)
12
Logical theories
In a theory, we study validity of FOL arguments under some specializedassumptions (called axioms).
Example 1.4
Number theory uses symbols 0, 1, . . . , <, +,· with some special meaning(usually defined by some axioms)
The following sentence makes little sense until we assign the specialmeanings to > and ·.
∀x∃p.(p > x ∧ (∀v1. (v1 > 1⇒ ∀v2. p 6= v1 · v2)))
Under the meanings it says that there are arbitrarily large prime numbers.
In the previous example, we had a vague understanding of predicatex is human (H(x)). Here we precisely know what is predicate x < y .
![Page 36: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/36.jpg)
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Logical theories II
The logical theories are useful in studying specialized domains.
Logic was thought to be immensely useful general purpose tool in studyingproperties of various mathematical domains.
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Logical theories II
The logical theories are useful in studying specialized domains.
Logic was thought to be immensely useful general purpose tool in studyingproperties of various mathematical domains.
![Page 38: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/38.jpg)
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Incompleteness results
But sadly, one can prove that
Theorem 1.1 (Godel’s incompleteness)
There are theories whose assumptions can not be listed.
Proof sketch.The proof proceeds by showing that if there is such a list for number theorythen the list can prove a theorem that “the list cannot prove the theorem”.Contradiction.
The incompleteness was considered epic failure of logic as a tool to do math.
From the ashes of logic, rose computer science.
![Page 39: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/39.jpg)
14
Incompleteness results
But sadly, one can prove that
Theorem 1.1 (Godel’s incompleteness)
There are theories whose assumptions can not be listed.
Proof sketch.The proof proceeds by showing that if there is such a list for number theorythen the list can prove a theorem that “the list cannot prove the theorem”.Contradiction.
The incompleteness was considered epic failure of logic as a tool to do math.
From the ashes of logic, rose computer science.
![Page 40: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/40.jpg)
14
Incompleteness results
But sadly, one can prove that
Theorem 1.1 (Godel’s incompleteness)
There are theories whose assumptions can not be listed.
Proof sketch.The proof proceeds by showing that if there is such a list for number theorythen the list can prove a theorem that “the list cannot prove the theorem”.Contradiction.
The incompleteness was considered epic failure of logic as a tool to do math.
From the ashes of logic, rose computer science.
![Page 41: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/41.jpg)
14
Incompleteness results
But sadly, one can prove that
Theorem 1.1 (Godel’s incompleteness)
There are theories whose assumptions can not be listed.
Proof sketch.The proof proceeds by showing that if there is such a list for number theorythen the list can prove a theorem that “the list cannot prove the theorem”.Contradiction.
The incompleteness was considered epic failure of logic as a tool to do math.
From the ashes of logic, rose computer science.
![Page 42: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/42.jpg)
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Section 1.3
Course Logistics
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Evaluation
I Assignments : 25%, 5% each (strict deadlines!)
I Midterm : 20% (1 hour)
I Presentation: 15% (15 min)
I Final: 40% (2 hour)
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Website
Look at the following website for the further information
http://www.tcs.tifr.res.in/~agupta/courses/2015-logic
All the assignments and slides will be posted at the website.
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A puzzle that melt the internet!!To warm up your logic skills, solve the following puzzle:
Sanjay and Salman just become friends with Madhuri, and they want toknow when her birthday is. Madhuri gives them a list of possible dates.March 14, March 15, March 18,April 16, April 17,May 13, May 15,June 13, June 14, June 16
Madhuri then tells Sanjay and Salman separately the month and the day ofher birthday respectively.
Sanjay: I don’t know when her birthday is, but I know that Salman doesn’tknow too.Salman: At first I don’t know when her birthday is, but I know now.Sanjay: Then I also know when her birthday is.
So when is Madhuri’s birthday?
5 min break!
![Page 46: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/46.jpg)
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A puzzle that melt the internet!!To warm up your logic skills, solve the following puzzle:
Sanjay and Salman just become friends with Madhuri, and they want toknow when her birthday is. Madhuri gives them a list of possible dates.March 14, March 15, March 18,April 16, April 17,May 13, May 15,June 13, June 14, June 16
Madhuri then tells Sanjay and Salman separately the month and the day ofher birthday respectively.
Sanjay: I don’t know when her birthday is, but I know that Salman doesn’tknow too.Salman: At first I don’t know when her birthday is, but I know now.Sanjay: Then I also know when her birthday is.
So when is Madhuri’s birthday? 5 min break!
![Page 47: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/47.jpg)
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Section 1.4
Tuples,Sets,Vectors,Functions,Relations
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20
Tuples
A tuple is a finite ordered list of elements. e.g., (a1, . . . , an) is an n-tuple.
Typical usage,
I immutable
I Access entries by assigning distinct names or by ith-component
I used to represent objects that are built using a known small finitenumber of components. e.g., automata, etc.
I In particular, (a, b) denotes a pair, (a, b, c) denotes a triple
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20
Tuples
A tuple is a finite ordered list of elements. e.g., (a1, . . . , an) is an n-tuple.
Typical usage,
I immutable
I Access entries by assigning distinct names or by ith-component
I used to represent objects that are built using a known small finitenumber of components. e.g., automata, etc.
I In particular, (a, b) denotes a pair, (a, b, c) denotes a triple
![Page 50: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/50.jpg)
20
Tuples
A tuple is a finite ordered list of elements. e.g., (a1, . . . , an) is an n-tuple.
Typical usage,
I immutable
I Access entries by assigning distinct names or by ith-component
I used to represent objects that are built using a known small finitenumber of components. e.g., automata, etc.
I In particular, (a, b) denotes a pair, (a, b, c) denotes a triple
![Page 51: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/51.jpg)
21
Sets
I A set is a collection of things. e. g., S = {a, b, c}
I a ∈ S denotes a is an element of set S
I d /∈ S denotes d is not an element of set S
I ∅ denotes a set without any element (Note: ∅ 6= {∅})I {x |P(x)} denotes the set of elements that satisfy predicate P
I A ⊆ B denotes B contains all the elements of A.
I A ⊂ B denotes B ⊆ A and B 6= A.
I 2A := {B|B ⊆ A}I |A| denotes carnality (or size) of A.
Exercise 1.1
I {f (x)|P(x)} =
{y |there is x such that y = f (x) and P(x)}
I {x ∈ D|P(x)} =?
I {x}∪{f (x , y)|P(x , y)} =?
I {3|3 < 0} =?
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21
Sets
I A set is a collection of things. e. g., S = {a, b, c}I a ∈ S denotes a is an element of set S
I d /∈ S denotes d is not an element of set S
I ∅ denotes a set without any element (Note: ∅ 6= {∅})I {x |P(x)} denotes the set of elements that satisfy predicate P
I A ⊆ B denotes B contains all the elements of A.
I A ⊂ B denotes B ⊆ A and B 6= A.
I 2A := {B|B ⊆ A}I |A| denotes carnality (or size) of A.
Exercise 1.1
I {f (x)|P(x)} =
{y |there is x such that y = f (x) and P(x)}
I {x ∈ D|P(x)} =?
I {x}∪{f (x , y)|P(x , y)} =?
I {3|3 < 0} =?
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21
Sets
I A set is a collection of things. e. g., S = {a, b, c}I a ∈ S denotes a is an element of set S
I d /∈ S denotes d is not an element of set S
I ∅ denotes a set without any element (Note: ∅ 6= {∅})
I {x |P(x)} denotes the set of elements that satisfy predicate P
I A ⊆ B denotes B contains all the elements of A.
I A ⊂ B denotes B ⊆ A and B 6= A.
I 2A := {B|B ⊆ A}I |A| denotes carnality (or size) of A.
Exercise 1.1
I {f (x)|P(x)} =
{y |there is x such that y = f (x) and P(x)}
I {x ∈ D|P(x)} =?
I {x}∪{f (x , y)|P(x , y)} =?
I {3|3 < 0} =?
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21
Sets
I A set is a collection of things. e. g., S = {a, b, c}I a ∈ S denotes a is an element of set S
I d /∈ S denotes d is not an element of set S
I ∅ denotes a set without any element (Note: ∅ 6= {∅})I {x |P(x)} denotes the set of elements that satisfy predicate P
I A ⊆ B denotes B contains all the elements of A.
I A ⊂ B denotes B ⊆ A and B 6= A.
I 2A := {B|B ⊆ A}I |A| denotes carnality (or size) of A.
Exercise 1.1
I {f (x)|P(x)} =
{y |there is x such that y = f (x) and P(x)}
I {x ∈ D|P(x)} =?
I {x}∪{f (x , y)|P(x , y)} =?
I {3|3 < 0} =?
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21
Sets
I A set is a collection of things. e. g., S = {a, b, c}I a ∈ S denotes a is an element of set S
I d /∈ S denotes d is not an element of set S
I ∅ denotes a set without any element (Note: ∅ 6= {∅})I {x |P(x)} denotes the set of elements that satisfy predicate P
I A ⊆ B denotes B contains all the elements of A.
I A ⊂ B denotes B ⊆ A and B 6= A.
I 2A := {B|B ⊆ A}I |A| denotes carnality (or size) of A.
Exercise 1.1
I {f (x)|P(x)} =
{y |there is x such that y = f (x) and P(x)}
I {x ∈ D|P(x)} =?
I {x}∪{f (x , y)|P(x , y)} =?
I {3|3 < 0} =?
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21
Sets
I A set is a collection of things. e. g., S = {a, b, c}I a ∈ S denotes a is an element of set S
I d /∈ S denotes d is not an element of set S
I ∅ denotes a set without any element (Note: ∅ 6= {∅})I {x |P(x)} denotes the set of elements that satisfy predicate P
I A ⊆ B denotes B contains all the elements of A.
I A ⊂ B denotes B ⊆ A and B 6= A.
I 2A := {B|B ⊆ A}I |A| denotes carnality (or size) of A.
Exercise 1.1
I {f (x)|P(x)} =
{y |there is x such that y = f (x) and P(x)}
I {x ∈ D|P(x)} =?
I {x}∪{f (x , y)|P(x , y)} =?
I {3|3 < 0} =?
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21
Sets
I A set is a collection of things. e. g., S = {a, b, c}I a ∈ S denotes a is an element of set S
I d /∈ S denotes d is not an element of set S
I ∅ denotes a set without any element (Note: ∅ 6= {∅})I {x |P(x)} denotes the set of elements that satisfy predicate P
I A ⊆ B denotes B contains all the elements of A.
I A ⊂ B denotes B ⊆ A and B 6= A.
I 2A := {B|B ⊆ A}I |A| denotes carnality (or size) of A.
Exercise 1.1
I {f (x)|P(x)} =
{y |there is x such that y = f (x) and P(x)}
I {x ∈ D|P(x)} =?
I {x}∪{f (x , y)|P(x , y)} =?
I {3|3 < 0} =?
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21
Sets
I A set is a collection of things. e. g., S = {a, b, c}I a ∈ S denotes a is an element of set S
I d /∈ S denotes d is not an element of set S
I ∅ denotes a set without any element (Note: ∅ 6= {∅})I {x |P(x)} denotes the set of elements that satisfy predicate P
I A ⊆ B denotes B contains all the elements of A.
I A ⊂ B denotes B ⊆ A and B 6= A.
I 2A := {B|B ⊆ A}I |A| denotes carnality (or size) of A.
Exercise 1.1
I {f (x)|P(x)} = {y |there is x such that y = f (x) and P(x)}I {x ∈ D|P(x)} =?
I {x}∪{f (x , y)|P(x , y)} =?
I {3|3 < 0} =?
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22
Set operations
I A∪B := {x |x ∈ A or x ∈ B}I A∩B := {x |x ∈ A and x ∈ B}I A \ B := {x |x ∈ A and x /∈ B}
I ∪ and ∩ are reflexive, symmetric, and transitive
I A∪∅ = A, A∩∅ = ∅, A∪A = A, and A∩A = A
I (A∩B)∪C = (A∪C )∩(B∪C ), and (A∪B)∩C = (A∩C )∪(B∩C )
I For some set U (known as universal set), A := U \ A
I (A∪B) = (A∪B) and (A∩B) = (A∩B)
I A∪A = U and A∩A = ∅I For n > 1, and sets A1,. . . , An,
A1 × · · · × An := {(a1, . . . , an)|for each i ∈ 1..n, ai ∈ Ai}
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Set operations
I A∪B := {x |x ∈ A or x ∈ B}I A∩B := {x |x ∈ A and x ∈ B}I A \ B := {x |x ∈ A and x /∈ B}I ∪ and ∩ are reflexive, symmetric, and transitive
I A∪∅ = A, A∩∅ = ∅, A∪A = A, and A∩A = A
I (A∩B)∪C = (A∪C )∩(B∪C ), and (A∪B)∩C = (A∩C )∪(B∩C )
I For some set U (known as universal set), A := U \ A
I (A∪B) = (A∪B) and (A∩B) = (A∩B)
I A∪A = U and A∩A = ∅I For n > 1, and sets A1,. . . , An,
A1 × · · · × An := {(a1, . . . , an)|for each i ∈ 1..n, ai ∈ Ai}
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Set operations
I A∪B := {x |x ∈ A or x ∈ B}I A∩B := {x |x ∈ A and x ∈ B}I A \ B := {x |x ∈ A and x /∈ B}I ∪ and ∩ are reflexive, symmetric, and transitive
I A∪∅ = A, A∩∅ = ∅, A∪A = A, and A∩A = A
I (A∩B)∪C = (A∪C )∩(B∪C ), and (A∪B)∩C = (A∩C )∪(B∩C )
I For some set U (known as universal set), A := U \ A
I (A∪B) = (A∪B) and (A∩B) = (A∩B)
I A∪A = U and A∩A = ∅I For n > 1, and sets A1,. . . , An,
A1 × · · · × An := {(a1, . . . , an)|for each i ∈ 1..n, ai ∈ Ai}
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Set operations
I A∪B := {x |x ∈ A or x ∈ B}I A∩B := {x |x ∈ A and x ∈ B}I A \ B := {x |x ∈ A and x /∈ B}I ∪ and ∩ are reflexive, symmetric, and transitive
I A∪∅ = A, A∩∅ = ∅, A∪A = A, and A∩A = A
I (A∩B)∪C = (A∪C )∩(B∪C ), and (A∪B)∩C = (A∩C )∪(B∩C )
I For some set U (known as universal set), A := U \ A
I (A∪B) = (A∪B) and (A∩B) = (A∩B)
I A∪A = U and A∩A = ∅
I For n > 1, and sets A1,. . . , An,
A1 × · · · × An := {(a1, . . . , an)|for each i ∈ 1..n, ai ∈ Ai}
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Set operations
I A∪B := {x |x ∈ A or x ∈ B}I A∩B := {x |x ∈ A and x ∈ B}I A \ B := {x |x ∈ A and x /∈ B}I ∪ and ∩ are reflexive, symmetric, and transitive
I A∪∅ = A, A∩∅ = ∅, A∪A = A, and A∩A = A
I (A∩B)∪C = (A∪C )∩(B∪C ), and (A∪B)∩C = (A∩C )∪(B∩C )
I For some set U (known as universal set), A := U \ A
I (A∪B) = (A∪B) and (A∩B) = (A∩B)
I A∪A = U and A∩A = ∅I For n > 1, and sets A1,. . . , An,
A1 × · · · × An := {(a1, . . . , an)|for each i ∈ 1..n, ai ∈ Ai}
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More set notations
I Let S be a set of sets,⋃S := {x | there is A s.t. x ∈ A and A ∈ S}∗
I Let S be a function from N to sets,⋃i∈J
S(i) :=⋃{S(i)|i ∈ J}
I Analogous notations for⋂
∗s.t. stands for “such that”
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More set notations
I Let S be a set of sets,⋃S := {x | there is A s.t. x ∈ A and A ∈ S}∗
I Let S be a function from N to sets,⋃i∈J
S(i) :=⋃{S(i)|i ∈ J}
I Analogous notations for⋂
∗s.t. stands for “such that”
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More set notations
I Let S be a set of sets,⋃S := {x | there is A s.t. x ∈ A and A ∈ S}∗
I Let S be a function from N to sets,⋃i∈J
S(i) :=⋃{S(i)|i ∈ J}
I Analogous notations for⋂
∗s.t. stands for “such that”
![Page 67: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/67.jpg)
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Relations
I A relation R between A and B is R ⊆ A× B
I ∆A := {(x , x)|x ∈ A} and R−1 = {(y , x)|(x , y) ∈ R}I R◦S := {(x , z)| there is y s.t. (x , y) ∈ R and (y , z) ∈ S}I dom(R) := {x |(x , y) ∈ R} and range(R) := {y |(x , y) ∈ R}I (A,R) often viewed as a directed graph
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Relations
I A relation R between A and B is R ⊆ A× B
I ∆A := {(x , x)|x ∈ A} and R−1 = {(y , x)|(x , y) ∈ R}
I R◦S := {(x , z)| there is y s.t. (x , y) ∈ R and (y , z) ∈ S}I dom(R) := {x |(x , y) ∈ R} and range(R) := {y |(x , y) ∈ R}I (A,R) often viewed as a directed graph
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Relations
I A relation R between A and B is R ⊆ A× B
I ∆A := {(x , x)|x ∈ A} and R−1 = {(y , x)|(x , y) ∈ R}I R◦S := {(x , z)| there is y s.t. (x , y) ∈ R and (y , z) ∈ S}
I dom(R) := {x |(x , y) ∈ R} and range(R) := {y |(x , y) ∈ R}I (A,R) often viewed as a directed graph
![Page 70: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/70.jpg)
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Relations
I A relation R between A and B is R ⊆ A× B
I ∆A := {(x , x)|x ∈ A} and R−1 = {(y , x)|(x , y) ∈ R}I R◦S := {(x , z)| there is y s.t. (x , y) ∈ R and (y , z) ∈ S}I dom(R) := {x |(x , y) ∈ R} and range(R) := {y |(x , y) ∈ R}I (A,R) often viewed as a directed graph
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Transtive closure
Let R ⊆ A× A
I R0 := ∆A and Rn+1 := R◦Rn
I Transitive closure R∗ :=⋃
n≥0Rn
I Lemma: R∗ is the least relation S that satisfies
∆A∪(R◦S) ⊆ S
I In other words, R∗ is least fixed point(lfp) of f (S) := ∆A∪(R◦S)
I Let R+ :=⋃
n>0Rn
Exercise 1.2R+ is lfp of ? Is the lfp unique ?
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25
Transtive closure
Let R ⊆ A× A
I R0 := ∆A and Rn+1 := R◦Rn
I Transitive closure R∗ :=⋃
n≥0Rn
I Lemma: R∗ is the least relation S that satisfies
∆A∪(R◦S) ⊆ S
I In other words, R∗ is least fixed point(lfp) of f (S) := ∆A∪(R◦S)
I Let R+ :=⋃
n>0Rn
Exercise 1.2R+ is lfp of ? Is the lfp unique ?
![Page 73: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/73.jpg)
25
Transtive closure
Let R ⊆ A× A
I R0 := ∆A and Rn+1 := R◦Rn
I Transitive closure R∗ :=⋃
n≥0Rn
I Lemma: R∗ is the least relation S that satisfies
∆A∪(R◦S) ⊆ S
I In other words, R∗ is least fixed point(lfp) of f (S) := ∆A∪(R◦S)
I Let R+ :=⋃
n>0Rn
Exercise 1.2R+ is lfp of ? Is the lfp unique ?
![Page 74: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/74.jpg)
25
Transtive closure
Let R ⊆ A× A
I R0 := ∆A and Rn+1 := R◦Rn
I Transitive closure R∗ :=⋃
n≥0Rn
I Lemma: R∗ is the least relation S that satisfies
∆A∪(R◦S) ⊆ S
I In other words, R∗ is least fixed point(lfp) of f (S) := ∆A∪(R◦S)
I Let R+ :=⋃
n>0Rn
Exercise 1.2R+ is lfp of ? Is the lfp unique ?
![Page 75: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/75.jpg)
25
Transtive closure
Let R ⊆ A× A
I R0 := ∆A and Rn+1 := R◦Rn
I Transitive closure R∗ :=⋃
n≥0Rn
I Lemma: R∗ is the least relation S that satisfies
∆A∪(R◦S) ⊆ S
I In other words, R∗ is least fixed point(lfp) of f (S) := ∆A∪(R◦S)
I Let R+ :=⋃
n>0Rn
Exercise 1.2R+ is lfp of ? Is the lfp unique ?
![Page 76: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/76.jpg)
25
Transtive closure
Let R ⊆ A× A
I R0 := ∆A and Rn+1 := R◦Rn
I Transitive closure R∗ :=⋃
n≥0Rn
I Lemma: R∗ is the least relation S that satisfies
∆A∪(R◦S) ⊆ S
I In other words, R∗ is least fixed point(lfp) of f (S) := ∆A∪(R◦S)
I Let R+ :=⋃
n>0Rn
Exercise 1.2R+ is lfp of ? Is the lfp unique ?
![Page 77: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/77.jpg)
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Some equivalences
I (R1◦R2)◦R3 = R1◦(R2◦R3)
I (R1◦R2)−1 = R−12 ◦R−11
I (R1∪R2)◦R3 = (R1◦R2)∪(R2◦R3)
I R∗−1 = R−1∗
![Page 78: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/78.jpg)
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Equivalence Relation
E ⊆ A× A is an equivalence relation if
I reflexive: ∆A ⊆ E
I symmetric: E = E−1
I transitive: E◦E ⊆ E
E defines a collection of sets that are partitions of A
A/E = {{y |(x , y) ∈ E}|x ∈ A}
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Functions
f ⊆ A× B is a function if
I dom(f ) = A
I For each x , y , and z , if (x , y) ∈ f and (x , z) ∈ f then y = z
Let A→ B denote set of all functions from A to B,and f : A→ B denote that f is in A→ B.
Function update: f [x 7→ y ] creates a new function such that
f [x 7→ y ](z) :=
{y if z = x
f (z) if otherwise.
For set A′ ⊆ A, let f |A′ denote a function in A′ → B and for each x ∈ A′,f |A′(x) = f (x) one-to-one: for each x and y in A, if x 6= y then f (x) 6= f (y)onto : for each x ∈ A there is a y ∈ B such that x = f (y)
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Functions
f ⊆ A× B is a function if
I dom(f ) = A
I For each x , y , and z , if (x , y) ∈ f and (x , z) ∈ f then y = z
Let A→ B denote set of all functions from A to B,and f : A→ B denote that f is in A→ B.
Function update: f [x 7→ y ] creates a new function such that
f [x 7→ y ](z) :=
{y if z = x
f (z) if otherwise.
For set A′ ⊆ A, let f |A′ denote a function in A′ → B and for each x ∈ A′,f |A′(x) = f (x) one-to-one: for each x and y in A, if x 6= y then f (x) 6= f (y)onto : for each x ∈ A there is a y ∈ B such that x = f (y)
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Functions
f ⊆ A× B is a function if
I dom(f ) = A
I For each x , y , and z , if (x , y) ∈ f and (x , z) ∈ f then y = z
Let A→ B denote set of all functions from A to B,and f : A→ B denote that f is in A→ B.
Function update: f [x 7→ y ] creates a new function such that
f [x 7→ y ](z) :=
{y if z = x
f (z) if otherwise.
For set A′ ⊆ A, let f |A′ denote a function in A′ → B and for each x ∈ A′,f |A′(x) = f (x)
one-to-one: for each x and y in A, if x 6= y then f (x) 6= f (y)onto : for each x ∈ A there is a y ∈ B such that x = f (y)
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28
Functions
f ⊆ A× B is a function if
I dom(f ) = A
I For each x , y , and z , if (x , y) ∈ f and (x , z) ∈ f then y = z
Let A→ B denote set of all functions from A to B,and f : A→ B denote that f is in A→ B.
Function update: f [x 7→ y ] creates a new function such that
f [x 7→ y ](z) :=
{y if z = x
f (z) if otherwise.
For set A′ ⊆ A, let f |A′ denote a function in A′ → B and for each x ∈ A′,f |A′(x) = f (x) one-to-one: for each x and y in A, if x 6= y then f (x) 6= f (y)onto : for each x ∈ A there is a y ∈ B such that x = f (y)
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Partial function
f ⊆ A× B is a function if
I dom(f ) = A
I For each x , y , and z , if (x , y) ∈ f and (x , z) ∈ f then y = z
Let A ↪→ B denote set of all partial functions from A to B,and f : A ↪→ B denote that f is in A ↪→ B.
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Relation as functions
Let R ⊆ A× B, we can view R as a function of type A→ 2B .
R(a) ::= {b|(a, b) ∈ R}
We can also view R as a function of type 2A → 2B .
R(S) :=⋃{R(a)|a ∈ S}
We will use the above notations interchangeably.
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Strings or Sequences
Let Σ be a set of symbols.
For si ∈ Σ, s1 . . . sn be a finite string/word/sequence of symbols from Σ
Let Σ∗ be the set of finite strings from Σ
Let ε denote the empty string
For v ∈ Σ∗ and w ∈ Σ∗, let vw denote the concatenation of v and w
We say u is a prefix of w if uv = w for some v .
We say u is a proper prefix of w if uv = w for some non-empty v .
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Strings or Sequences
Let Σ be a set of symbols.
For si ∈ Σ, s1 . . . sn be a finite string/word/sequence of symbols from Σ
Let Σ∗ be the set of finite strings from Σ
Let ε denote the empty string
For v ∈ Σ∗ and w ∈ Σ∗, let vw denote the concatenation of v and w
We say u is a prefix of w if uv = w for some v .
We say u is a proper prefix of w if uv = w for some non-empty v .
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31
Strings or Sequences
Let Σ be a set of symbols.
For si ∈ Σ, s1 . . . sn be a finite string/word/sequence of symbols from Σ
Let Σ∗ be the set of finite strings from Σ
Let ε denote the empty string
For v ∈ Σ∗ and w ∈ Σ∗, let vw denote the concatenation of v and w
We say u is a prefix of w if uv = w for some v .
We say u is a proper prefix of w if uv = w for some non-empty v .
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31
Strings or Sequences
Let Σ be a set of symbols.
For si ∈ Σ, s1 . . . sn be a finite string/word/sequence of symbols from Σ
Let Σ∗ be the set of finite strings from Σ
Let ε denote the empty string
For v ∈ Σ∗ and w ∈ Σ∗, let vw denote the concatenation of v and w
We say u is a prefix of w if uv = w for some v .
We say u is a proper prefix of w if uv = w for some non-empty v .
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31
Strings or Sequences
Let Σ be a set of symbols.
For si ∈ Σ, s1 . . . sn be a finite string/word/sequence of symbols from Σ
Let Σ∗ be the set of finite strings from Σ
Let ε denote the empty string
For v ∈ Σ∗ and w ∈ Σ∗, let vw denote the concatenation of v and w
We say u is a prefix of w if uv = w for some v .
We say u is a proper prefix of w if uv = w for some non-empty v .
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31
Strings or Sequences
Let Σ be a set of symbols.
For si ∈ Σ, s1 . . . sn be a finite string/word/sequence of symbols from Σ
Let Σ∗ be the set of finite strings from Σ
Let ε denote the empty string
For v ∈ Σ∗ and w ∈ Σ∗, let vw denote the concatenation of v and w
We say u is a prefix of w if uv = w for some v .
We say u is a proper prefix of w if uv = w for some non-empty v .
![Page 91: Mathematical Logic Lecture 1: Introduction and background](https://reader034.fdocuments.in/reader034/viewer/2022050612/627428f15d170c6fd21bc610/html5/thumbnails/91.jpg)
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Homomorphism
We call two sets isomorphic if we can demonstrate that there is atransformation from one to another preserving the key properties of interest.
For example, Consider two sets A and B. Let the properties of interest are
PA ⊆ A× · · · × A︸ ︷︷ ︸n
and PB ⊆ B × · · · × B︸ ︷︷ ︸n
.
Definition 1.1A function h : A→ B is a homomorphism w.r.t. properties PA and PB if
(a1, . . . , an) ∈ PA iff (h(a1), . . . , h(an)) ∈ PB
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32
Homomorphism
We call two sets isomorphic if we can demonstrate that there is atransformation from one to another preserving the key properties of interest.
For example, Consider two sets A and B. Let the properties of interest are
PA ⊆ A× · · · × A︸ ︷︷ ︸n
and PB ⊆ B × · · · × B︸ ︷︷ ︸n
.
Definition 1.1A function h : A→ B is a homomorphism w.r.t. properties PA and PB if
(a1, . . . , an) ∈ PA iff (h(a1), . . . , h(an)) ∈ PB
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32
Homomorphism
We call two sets isomorphic if we can demonstrate that there is atransformation from one to another preserving the key properties of interest.
For example, Consider two sets A and B. Let the properties of interest are
PA ⊆ A× · · · × A︸ ︷︷ ︸n
and PB ⊆ B × · · · × B︸ ︷︷ ︸n
.
Definition 1.1A function h : A→ B is a homomorphism w.r.t. properties PA and PB if
(a1, . . . , an) ∈ PA iff (h(a1), . . . , h(an)) ∈ PB
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Isomorphism/isomorphic
Definition 1.2If h is one-to-one then it is called an isomorphism.If h is also onto then A and B are considered isomorphic.
Exercise 1.3Prove A→ (B → C ) is isomorphic to A× B → C .
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Isomorphism/isomorphic
Definition 1.2If h is one-to-one then it is called an isomorphism.If h is also onto then A and B are considered isomorphic.
Exercise 1.3Prove A→ (B → C ) is isomorphic to A× B → C .
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Section 1.5
Cardinality
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35
Comparing sizes of sets
Cordiality is a measure of the number of elements of the set, which isdenoted by |A| for set A. Non-trivial to understand if |A| is not finite.
Definition 1.3|A| ≤ |B| if there is a one-to-one f : A→ B
Theorem 1.2If f is also onto then |A| = |B|.
Exercise 1.4Prove theorem 1.2
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35
Comparing sizes of sets
Cordiality is a measure of the number of elements of the set, which isdenoted by |A| for set A. Non-trivial to understand if |A| is not finite.
Definition 1.3|A| ≤ |B| if there is a one-to-one f : A→ B
Theorem 1.2If f is also onto then |A| = |B|.
Exercise 1.4Prove theorem 1.2
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35
Comparing sizes of sets
Cordiality is a measure of the number of elements of the set, which isdenoted by |A| for set A. Non-trivial to understand if |A| is not finite.
Definition 1.3|A| ≤ |B| if there is a one-to-one f : A→ B
Theorem 1.2If f is also onto then |A| = |B|.
Exercise 1.4Prove theorem 1.2
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Countable/uncountable
Definition 1.4A is countable if |A| ≤ |N|
Definition 1.5A is uncountable if |A| > |N|
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Countable/uncountable
Definition 1.4A is countable if |A| ≤ |N|
Definition 1.5A is uncountable if |A| > |N|
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37
Countable finite words
Theorem 1.3If Σ is countable, then Σ∗ is countable
Proof.Since Σ is countable there exists one-to-one f : Σ→ N.We need to find a one-to-one h : Σ∗ → N.
Let pi be the ith prime. Our choice of h is
h(a1 . . . an) = Πi∈1..npf (ai )i .
Exercise 1.5Show h is one-to-one.
How to prove?Find a one-to-one map to N
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37
Countable finite words
Theorem 1.3If Σ is countable, then Σ∗ is countable
Proof.Since Σ is countable there exists one-to-one f : Σ→ N.We need to find a one-to-one h : Σ∗ → N.
Let pi be the ith prime. Our choice of h is
h(a1 . . . an) = Πi∈1..npf (ai )i .
Exercise 1.5Show h is one-to-one.
How to prove?Find a one-to-one map to N
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37
Countable finite words
Theorem 1.3If Σ is countable, then Σ∗ is countable
Proof.Since Σ is countable there exists one-to-one f : Σ→ N.We need to find a one-to-one h : Σ∗ → N.
Let pi be the ith prime. Our choice of h is
h(a1 . . . an) = Πi∈1..npf (ai )i .
Exercise 1.5Show h is one-to-one.
How to prove?Find a one-to-one map to N
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37
Countable finite words
Theorem 1.3If Σ is countable, then Σ∗ is countable
Proof.Since Σ is countable there exists one-to-one f : Σ→ N.We need to find a one-to-one h : Σ∗ → N.
Let pi be the ith prime. Our choice of h is
h(a1 . . . an) = Πi∈1..npf (ai )i .
Exercise 1.5Show h is one-to-one.
How to prove?Find a one-to-one map to N
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38
Cantor’s theorem
Theorem 1.4|A| < |2A|
Proof.Consider function h(a) = {a}. h is one-to-one function.There is a one-to-one function in A→ 2A, therefore |A| ≤ |2A|.
To show strictness, we need to show that there is no one-to-one and ontofunction in A→ 2A.
Let us suppose f : A→ 2A is one-to-one and onto.Consider, S = {a|a 6∈ f (a)}. Since f is onto, there is a b s.t. f (b) = S .Case b ∈ S : Therefore, b ∈ f (b). Due to def. S , b /∈ S . Contradiction.Case b /∈ S : Therefore, b /∈ f (b). Due to def. S , b ∈ S . Contradiction.
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38
Cantor’s theorem
Theorem 1.4|A| < |2A|
Proof.Consider function h(a) = {a}. h is one-to-one function.There is a one-to-one function in A→ 2A, therefore |A| ≤ |2A|.
To show strictness, we need to show that there is no one-to-one and ontofunction in A→ 2A.
Let us suppose f : A→ 2A is one-to-one and onto.Consider, S = {a|a 6∈ f (a)}. Since f is onto, there is a b s.t. f (b) = S .Case b ∈ S : Therefore, b ∈ f (b). Due to def. S , b /∈ S . Contradiction.Case b /∈ S : Therefore, b /∈ f (b). Due to def. S , b ∈ S . Contradiction.
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38
Cantor’s theorem
Theorem 1.4|A| < |2A|
Proof.Consider function h(a) = {a}. h is one-to-one function.There is a one-to-one function in A→ 2A, therefore |A| ≤ |2A|.
To show strictness, we need to show that there is no one-to-one and ontofunction in A→ 2A.
Let us suppose f : A→ 2A is one-to-one and onto.Consider, S = {a|a 6∈ f (a)}. Since f is onto, there is a b s.t. f (b) = S .Case b ∈ S : Therefore, b ∈ f (b). Due to def. S , b /∈ S . Contradiction.Case b /∈ S : Therefore, b /∈ f (b). Due to def. S , b ∈ S . Contradiction.
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38
Cantor’s theorem
Theorem 1.4|A| < |2A|
Proof.Consider function h(a) = {a}. h is one-to-one function.There is a one-to-one function in A→ 2A, therefore |A| ≤ |2A|.
To show strictness, we need to show that there is no one-to-one and ontofunction in A→ 2A.
Let us suppose f : A→ 2A is one-to-one and onto.
Consider, S = {a|a 6∈ f (a)}. Since f is onto, there is a b s.t. f (b) = S .Case b ∈ S : Therefore, b ∈ f (b). Due to def. S , b /∈ S . Contradiction.Case b /∈ S : Therefore, b /∈ f (b). Due to def. S , b ∈ S . Contradiction.
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38
Cantor’s theorem
Theorem 1.4|A| < |2A|
Proof.Consider function h(a) = {a}. h is one-to-one function.There is a one-to-one function in A→ 2A, therefore |A| ≤ |2A|.
To show strictness, we need to show that there is no one-to-one and ontofunction in A→ 2A.
Let us suppose f : A→ 2A is one-to-one and onto.Consider, S = {a|a 6∈ f (a)}. Since f is onto, there is a b s.t. f (b) = S .
Case b ∈ S : Therefore, b ∈ f (b). Due to def. S , b /∈ S . Contradiction.Case b /∈ S : Therefore, b /∈ f (b). Due to def. S , b ∈ S . Contradiction.
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38
Cantor’s theorem
Theorem 1.4|A| < |2A|
Proof.Consider function h(a) = {a}. h is one-to-one function.There is a one-to-one function in A→ 2A, therefore |A| ≤ |2A|.
To show strictness, we need to show that there is no one-to-one and ontofunction in A→ 2A.
Let us suppose f : A→ 2A is one-to-one and onto.Consider, S = {a|a 6∈ f (a)}. Since f is onto, there is a b s.t. f (b) = S .Case b ∈ S :
Therefore, b ∈ f (b). Due to def. S , b /∈ S . Contradiction.Case b /∈ S : Therefore, b /∈ f (b). Due to def. S , b ∈ S . Contradiction.
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38
Cantor’s theorem
Theorem 1.4|A| < |2A|
Proof.Consider function h(a) = {a}. h is one-to-one function.There is a one-to-one function in A→ 2A, therefore |A| ≤ |2A|.
To show strictness, we need to show that there is no one-to-one and ontofunction in A→ 2A.
Let us suppose f : A→ 2A is one-to-one and onto.Consider, S = {a|a 6∈ f (a)}. Since f is onto, there is a b s.t. f (b) = S .Case b ∈ S : Therefore, b ∈ f (b).
Due to def. S , b /∈ S . Contradiction.Case b /∈ S : Therefore, b /∈ f (b). Due to def. S , b ∈ S . Contradiction.
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38
Cantor’s theorem
Theorem 1.4|A| < |2A|
Proof.Consider function h(a) = {a}. h is one-to-one function.There is a one-to-one function in A→ 2A, therefore |A| ≤ |2A|.
To show strictness, we need to show that there is no one-to-one and ontofunction in A→ 2A.
Let us suppose f : A→ 2A is one-to-one and onto.Consider, S = {a|a 6∈ f (a)}. Since f is onto, there is a b s.t. f (b) = S .Case b ∈ S : Therefore, b ∈ f (b). Due to def. S , b /∈ S .
Contradiction.Case b /∈ S : Therefore, b /∈ f (b). Due to def. S , b ∈ S . Contradiction.
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38
Cantor’s theorem
Theorem 1.4|A| < |2A|
Proof.Consider function h(a) = {a}. h is one-to-one function.There is a one-to-one function in A→ 2A, therefore |A| ≤ |2A|.
To show strictness, we need to show that there is no one-to-one and ontofunction in A→ 2A.
Let us suppose f : A→ 2A is one-to-one and onto.Consider, S = {a|a 6∈ f (a)}. Since f is onto, there is a b s.t. f (b) = S .Case b ∈ S : Therefore, b ∈ f (b). Due to def. S , b /∈ S . Contradiction.
Case b /∈ S : Therefore, b /∈ f (b). Due to def. S , b ∈ S . Contradiction.
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38
Cantor’s theorem
Theorem 1.4|A| < |2A|
Proof.Consider function h(a) = {a}. h is one-to-one function.There is a one-to-one function in A→ 2A, therefore |A| ≤ |2A|.
To show strictness, we need to show that there is no one-to-one and ontofunction in A→ 2A.
Let us suppose f : A→ 2A is one-to-one and onto.Consider, S = {a|a 6∈ f (a)}. Since f is onto, there is a b s.t. f (b) = S .Case b ∈ S : Therefore, b ∈ f (b). Due to def. S , b /∈ S . Contradiction.Case b /∈ S : Therefore, b /∈ f (b). Due to def. S , b ∈ S . Contradiction.
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End of Lecture 1
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