Podcast
Questions and Answers
Given the complexities of defining a 'set,' particularly in mathematical logic, which of the following foundational issues poses the most significant challenge to the unbridled use of set theory in the formalization of all mathematical discourse?
Given the complexities of defining a 'set,' particularly in mathematical logic, which of the following foundational issues poses the most significant challenge to the unbridled use of set theory in the formalization of all mathematical discourse?
- The axiom of choice's non-constructive nature undermines predicativity.
- Gödel's incompleteness theorems demonstrate the impossibility of a complete and consistent axiomatization of set theory.
- The Löwenheim-Skolem theorem implies the existence of non-standard models of set theory, complicating semantic interpretations.
- Russell's paradox exposes the potential for inherent contradictions in unrestricted comprehension. (correct)
The axiom of extensionality in Zermelo-Fraenkel set theory (ZFC) definitively resolves all philosophical debates regarding the identity criteria for sets, thereby eliminating any ambiguity in determining whether two sets are, in fact, the same.
The axiom of extensionality in Zermelo-Fraenkel set theory (ZFC) definitively resolves all philosophical debates regarding the identity criteria for sets, thereby eliminating any ambiguity in determining whether two sets are, in fact, the same.
False (B)
Within the framework of axiomatic set theory, particularly ZFC, the construction of the von Neumann universe proceeds through transfinite recursion, indexed by ordinal numbers. At each stage $\alpha$, the set $V_{\alpha}$ is constructed by taking the power set of the previous stage. Formally, $V_{\alpha+1} =$ ____. The union of all such $V{\alpha}$ forms the von Neumann universe, a model for all of ZFC.
Within the framework of axiomatic set theory, particularly ZFC, the construction of the von Neumann universe proceeds through transfinite recursion, indexed by ordinal numbers. At each stage $\alpha$, the set $V_{\alpha}$ is constructed by taking the power set of the previous stage. Formally, $V_{\alpha+1} =$ ____. The union of all such $V{\alpha}$ forms the von Neumann universe, a model for all of ZFC.
$P(V_{\alpha})$
Consider a variant of Zermelo–Fraenkel set theory where the axiom of regularity is replaced with its negation. Describe a potential consequence of this modification concerning the structure of sets within this modified theory, paying particular attention to the existence (or non-existence) of well-founded sets.
Consider a variant of Zermelo–Fraenkel set theory where the axiom of regularity is replaced with its negation. Describe a potential consequence of this modification concerning the structure of sets within this modified theory, paying particular attention to the existence (or non-existence) of well-founded sets.
Match the following set-theoretic axioms with their corresponding intuitive descriptions:
Match the following set-theoretic axioms with their corresponding intuitive descriptions:
Given the subtleties involved in defining the 'power set' rigorously, what critical distinction must be maintained to avoid logical inconsistencies when applying the power set operation iteratively?
Given the subtleties involved in defining the 'power set' rigorously, what critical distinction must be maintained to avoid logical inconsistencies when applying the power set operation iteratively?
Zermelo-Fraenkel set theory with the axiom of choice (ZFC) categorically proves the well-ordering theorem, implying that every set, regardless of its cardinality or structure, can be placed in a well-ordered sequence; therefore, debates about the constructibility of such well-orderings are purely philosophical and have no bearing on the mathematical validity of the theorem within ZFC.
Zermelo-Fraenkel set theory with the axiom of choice (ZFC) categorically proves the well-ordering theorem, implying that every set, regardless of its cardinality or structure, can be placed in a well-ordered sequence; therefore, debates about the constructibility of such well-orderings are purely philosophical and have no bearing on the mathematical validity of the theorem within ZFC.
Tarski's theorem on the undefinability of truth demonstrates a fundamental limitation in formal systems. Specifically, if a formal system F is sufficiently strong (i.e., capable of interpreting arithmetic) and consistent, then the set of Gödel numbers of true sentences of F is not ____________ in F; that is, there is no formula within F that can correctly identify all and only the true sentences of F.
Tarski's theorem on the undefinability of truth demonstrates a fundamental limitation in formal systems. Specifically, if a formal system F is sufficiently strong (i.e., capable of interpreting arithmetic) and consistent, then the set of Gödel numbers of true sentences of F is not ____________ in F; that is, there is no formula within F that can correctly identify all and only the true sentences of F.
Describe the structural characteristics of sets constructed using the 'Cantor pairing function,' and outline a significant implication of this construction for understanding the cardinality of sets resulting from Cartesian products.
Describe the structural characteristics of sets constructed using the 'Cantor pairing function,' and outline a significant implication of this construction for understanding the cardinality of sets resulting from Cartesian products.
Match the following set operations with their corresponding set-builder notations, assuming $A$ and $B$ are arbitrary sets:
Match the following set operations with their corresponding set-builder notations, assuming $A$ and $B$ are arbitrary sets:
Given the role of 'Venn diagrams' in visualizing set relationships, what inherent limitation exists in their application when dealing with set operations involving a very large (e.g., uncountable) number of sets or sets with highly complex, non-intuitive relationships?
Given the role of 'Venn diagrams' in visualizing set relationships, what inherent limitation exists in their application when dealing with set operations involving a very large (e.g., uncountable) number of sets or sets with highly complex, non-intuitive relationships?
The principle of mathematical induction, while fundamental in number theory, is entirely inapplicable to proving properties of sets, since sets lack the inherent ordinal structure required for inductive arguments.
The principle of mathematical induction, while fundamental in number theory, is entirely inapplicable to proving properties of sets, since sets lack the inherent ordinal structure required for inductive arguments.
In the context of transfinite set theory, a cardinal number $\kappa$ is said to be __________ if it is greater than the cardinality of its power set, i.e., $\kappa > |P(\kappa)|$. The assertion that such cardinals exist is independent of ZFC.
In the context of transfinite set theory, a cardinal number $\kappa$ is said to be __________ if it is greater than the cardinality of its power set, i.e., $\kappa > |P(\kappa)|$. The assertion that such cardinals exist is independent of ZFC.
Outline the key differences between 'naive set theory' and axiomatic set theory (e.g., ZFC), focusing on how axiomatic approaches address the paradoxes that arise in the naive framework.
Outline the key differences between 'naive set theory' and axiomatic set theory (e.g., ZFC), focusing on how axiomatic approaches address the paradoxes that arise in the naive framework.
Match each set identity with the correct name
Match each set identity with the correct name
Considering the computational representation of 'sets' using bit strings, what inherent limitation arises when dealing with sets whose elements are drawn from an infinite universe?
Considering the computational representation of 'sets' using bit strings, what inherent limitation arises when dealing with sets whose elements are drawn from an infinite universe?
Given that computers can only manipulate finite data structures, the concept of representing 'infinite sets' within a computer is fundamentally flawed: any computational manipulation will necessarily operate on a finite approximation, thus distorting the true mathematical properties of the infinite set.
Given that computers can only manipulate finite data structures, the concept of representing 'infinite sets' within a computer is fundamentally flawed: any computational manipulation will necessarily operate on a finite approximation, thus distorting the true mathematical properties of the infinite set.
The 'continuum hypothesis' (CH) postulates that there is no set whose cardinality is strictly between that of the integers ($\aleph_0$) and the real numbers ($\mathfrak{c}$). Gödel proved that CH is consistent with ZFC, and Cohen later showed that the negation of CH is also consistent with ZFC, meaning that within ZFC, CH is ___________.
The 'continuum hypothesis' (CH) postulates that there is no set whose cardinality is strictly between that of the integers ($\aleph_0$) and the real numbers ($\mathfrak{c}$). Gödel proved that CH is consistent with ZFC, and Cohen later showed that the negation of CH is also consistent with ZFC, meaning that within ZFC, CH is ___________.
Explain the distinction between 'intensional definition' and 'extensional definition' of a set, and discuss why exclusively relying on extensional definitions can be problematic, especially when dealing with infinite sets or sets defined by complex properties.
Explain the distinction between 'intensional definition' and 'extensional definition' of a set, and discuss why exclusively relying on extensional definitions can be problematic, especially when dealing with infinite sets or sets defined by complex properties.
Match the following concepts related to set theory with their corresponding descriptions:
Match the following concepts related to set theory with their corresponding descriptions:
Given the iterative construction of the von Neumann universe in axiomatic set theory, which statement best reflects the significance of this construction for understanding the scope and limitations of ZFC?
Given the iterative construction of the von Neumann universe in axiomatic set theory, which statement best reflects the significance of this construction for understanding the scope and limitations of ZFC?
The 'axiom of regularity' (also known as the axiom of foundation) is indispensable for ensuring the consistency of Zermelo-Fraenkel set theory (ZFC); without it, the system becomes demonstrably inconsistent, leading to the derivation of logical contradictions.
The 'axiom of regularity' (also known as the axiom of foundation) is indispensable for ensuring the consistency of Zermelo-Fraenkel set theory (ZFC); without it, the system becomes demonstrably inconsistent, leading to the derivation of logical contradictions.
In the context of computability theory, if a set $A$ is such that both $A$ and its complement $A'$ are recursively enumerable (RE), then $A$ is said to be ____________. This implies the existence of an algorithm that can decide membership in $A$ in finite time.
In the context of computability theory, if a set $A$ is such that both $A$ and its complement $A'$ are recursively enumerable (RE), then $A$ is said to be ____________. This implies the existence of an algorithm that can decide membership in $A$ in finite time.
Explain the significance of the 'Banach-Tarski paradox' in the context of measure theory and set theory. How does it challenge our intuitive understanding of volume and set decomposition?
Explain the significance of the 'Banach-Tarski paradox' in the context of measure theory and set theory. How does it challenge our intuitive understanding of volume and set decomposition?
Match the following theorems and paradoxes with their primary implications for the foundations of mathematics and set theory:
Match the following theorems and paradoxes with their primary implications for the foundations of mathematics and set theory:
Considering the limitations of representing 'real numbers' within a computer system (due to their uncountability and the finite nature of computer memory), what potential consequence arises when performing complex arithmetic operations on these computationally represented 'real numbers'?
Considering the limitations of representing 'real numbers' within a computer system (due to their uncountability and the finite nature of computer memory), what potential consequence arises when performing complex arithmetic operations on these computationally represented 'real numbers'?
The Löwenheim-Skolem theorem implies that if Zermelo-Fraenkel set theory (ZFC) is consistent, it has a countable model; this definitively refutes Cantor's theorem, which establishes that the set of real numbers is uncountable, thereby demonstrating a fundamental contradiction in modern set theory.
The Löwenheim-Skolem theorem implies that if Zermelo-Fraenkel set theory (ZFC) is consistent, it has a countable model; this definitively refutes Cantor's theorem, which establishes that the set of real numbers is uncountable, thereby demonstrating a fundamental contradiction in modern set theory.
A relation $R$ on a set $A$ is an equivalence relation if it is reflexive, symmetric, and ___________. An equivalence relation partitions the set $A$ into disjoint equivalence classes.
A relation $R$ on a set $A$ is an equivalence relation if it is reflexive, symmetric, and ___________. An equivalence relation partitions the set $A$ into disjoint equivalence classes.
Explain the concept of 'diagonalization' as used in Cantor's proof that the real numbers are uncountable. How does this technique demonstrate the existence of real numbers that cannot be included in any given enumeration of the real numbers?
Explain the concept of 'diagonalization' as used in Cantor's proof that the real numbers are uncountable. How does this technique demonstrate the existence of real numbers that cannot be included in any given enumeration of the real numbers?
Match set operations with their corresponding bitwise computer string calculations, assuming strings of equal length:
Match set operations with their corresponding bitwise computer string calculations, assuming strings of equal length:
Given the connection between 'set theory' and 'formal languages' (e.g., describing the syntax and semantics of programming languages), what potential issue arises when using set theory to model inherently ambiguous or context-dependent features of natural languages?
Given the connection between 'set theory' and 'formal languages' (e.g., describing the syntax and semantics of programming languages), what potential issue arises when using set theory to model inherently ambiguous or context-dependent features of natural languages?
The 'Halting Problem,' which demonstrates the impossibility of creating a general algorithm to determine whether a given program will halt or run forever, has no direct implications for set theory because it is a problem specifically within computability theory, and set theory deals with abstract mathematical objects, not computational processes.
The 'Halting Problem,' which demonstrates the impossibility of creating a general algorithm to determine whether a given program will halt or run forever, has no direct implications for set theory because it is a problem specifically within computability theory, and set theory deals with abstract mathematical objects, not computational processes.
If $A$ and $B$ are sets, the set of all functions from B into A is denoted by $A^B$. The cardinality of $A^B$ is equal to $|A|^{|B|}$, where $|A|$ and $|B|$ denote the cardinalities of $A$ and $B$, respectively. If $A$ is the set of binary digits {0, 1}, then $A^B$ represents the set of all bit strings indexed by $B$. If B is the set of natural numbers N, then the the cardinality of $A^B$ is denoted by $2^{\aleph_0}$, which is equal to __________.
If $A$ and $B$ are sets, the set of all functions from B into A is denoted by $A^B$. The cardinality of $A^B$ is equal to $|A|^{|B|}$, where $|A|$ and $|B|$ denote the cardinalities of $A$ and $B$, respectively. If $A$ is the set of binary digits {0, 1}, then $A^B$ represents the set of all bit strings indexed by $B$. If B is the set of natural numbers N, then the the cardinality of $A^B$ is denoted by $2^{\aleph_0}$, which is equal to __________.
Describe a scenario in which the 'axiom of choice' is crucial for proving the existence of a mathematical object, but provides no means for explicitly constructing or identifying that object.
Describe a scenario in which the 'axiom of choice' is crucial for proving the existence of a mathematical object, but provides no means for explicitly constructing or identifying that object.
Match the following examples of sets with the correct set notation:
Match the following examples of sets with the correct set notation:
Given that 'computer representation of sets' often relies on bit strings, what specific challenge arises when attempting to represent sets of elements that are inherently continuous (e.g., intervals of real numbers)?
Given that 'computer representation of sets' often relies on bit strings, what specific challenge arises when attempting to represent sets of elements that are inherently continuous (e.g., intervals of real numbers)?
The fact that a computer can only manipulate a finite number of bits has no bearing on its ability to do set theory, because infinite sets can be described using finite descriptions within a programming language.
The fact that a computer can only manipulate a finite number of bits has no bearing on its ability to do set theory, because infinite sets can be described using finite descriptions within a programming language.
Let $A$ be a set. The power set of $A$, denoted $P(A)$ is the set of all _______ of $A$.
Let $A$ be a set. The power set of $A$, denoted $P(A)$ is the set of all _______ of $A$.
Define what it means for a set to be 'well-ordered,' and provide a non-trivial example of a well-ordered set that is not simply a finite set of integers.
Define what it means for a set to be 'well-ordered,' and provide a non-trivial example of a well-ordered set that is not simply a finite set of integers.
Match the different set operations given A = {1, 2, 3, 4, 5} and B = {3, 5, 6, 7}
Match the different set operations given A = {1, 2, 3, 4, 5} and B = {3, 5, 6, 7}
Flashcards
What is a set?
What is a set?
A well-defined, unordered collection of distinct objects, called elements or members.
What is set notation?
What is set notation?
Listing elements between braces to describe a set.
What is set-builder notation?
What is set-builder notation?
A notation that describes the properties that elements of the set have in common.
What is an empty set?
What is an empty set?
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What is a singleton set?
What is a singleton set?
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What is a subset?
What is a subset?
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What is a Power Set?
What is a Power Set?
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What are n-tuples?
What are n-tuples?
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What is a cartesian product?
What is a cartesian product?
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What is the union of sets?
What is the union of sets?
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What is the intersection of sets?
What is the intersection of sets?
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What are disjoint sets?
What are disjoint sets?
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What is the difference of sets?
What is the difference of sets?
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What is the complement of a set?
What is the complement of a set?
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What is the symmetric difference?
What is the symmetric difference?
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What are the Identity Laws?
What are the Identity Laws?
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What are the Domination Laws?
What are the Domination Laws?
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What are the Idempotent Laws?
What are the Idempotent Laws?
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What is the Complementation Law?
What is the Complementation Law?
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What are the Commutative Laws?
What are the Commutative Laws?
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What are the Associative Laws?
What are the Associative Laws?
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What are the Distributive Laws?
What are the Distributive Laws?
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What are De Morgan's Laws?
What are De Morgan's Laws?
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What are the Absorption Laws?
What are the Absorption Laws?
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What are the Complement Laws?
What are the Complement Laws?
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Bitwise Boolean OR
Bitwise Boolean OR
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Bitwise Boolean AND
Bitwise Boolean AND
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Study Notes
- Discrete Mathematics is the topic
- Maureen M. Villamor is the author
Topic Outline (Prelim)
- Sets
- Sequences and Summations
- Functions
- Binary Relations
- n-ary Relations
Objectives
- Describe sets using different notations
- Represent sets using a Venn diagram
- Derive the power set of a given set
- Compute for the Cartesian products of sets
- Perform operations on sets
- Show equality of sets using set identities
- Apply the addition principle to solve problems involving sets
- Represent sets using a computer
Definition of a Set
- A set is a well-defined, unordered collection of objects
- These objects are called elements or members of the set
- Example: the collection of all wooden chairs, the collection of real numbers between 0 and 1
Notation
- One way to describe a set that has a finite number of elements is by listing the elements between braces
- Example: the set of all positive integers less than four is A = {1, 2, 3}, the set of all vowels in the English alphabet is V = {a, e, i, o, u}
- Or, when the general pattern of the elements is obvious, not all members of the set need to be described; instead, an ellipses (...) is used
- Example: The set of positive integers less than 100 is P = {1, 2, 3, ..., 99}
- A set builder notation is used to describe a large finite or infinite set by specifying a property that the elements of the set have in common
- Example: B = { x | x is a positive integer less than 4 }, C = { x | x is a Computer Science student }
Useful Sets and Their Notations
- N = { x | x is a positive integer or zero } or N = { x | x is a natural number }
- Z = { x | x is an integer }
- Z+ = { x | x is a positive integer }
- Z- = { x | x is a negative integer }
- Q = { x | x is a rational number }
- R = { x | x is a real number }
- C = { x | x is a complex number }
- { } or Ø = empty set (set that has no elements)
Important Facts
- Sets can have unrelated elements, for example the set S = { r, 17, Raphael, Davao City }
- Repeated elements in a set can be ignored, for example A = { 1, 3, 2, 3, 1}
- The order in which the elements are listed is not important
- Example: A = { 1, 3, 2 } or A = { 3, 2, 1} or A = { 3, 1, 2 } or A = {2, 1, 3} are effectively the same
- Sets may contain other sets as members
- Example: A = { Ø, {a}, {b}, {a, b} }
Set Membership
- Use the symbol ∈ to denote membership
- For example, given A = { 1, 3, 5, 7}, 1 ∈ A and 3 ∈ A, but 2 ∉ A
- B = {x | x ∈ R and x² = -1 }
Equality of Sets
- Two sets A and B are equal if they have the same elements; that is if and only if for all x (x ∈ A if and only if x ∈ B). In this case, A = B
- Example: if A = {1, 2, 3} and B = { x | x ∈ Z+ and x² < 12 }, then A = B
Graphical Representation of Sets
- Sets are represented graphically using Venn diagrams
- Rectangle: Universal Set U
- Circles (or other geometric figures): sets
Singleton Set
- A set that only has one element
- A common mistake is to confuse the empty set Ø with the set { Ø }, which is a singleton set
- Therefore: Ø ≠ { Ø }
Subset
- Set A is a subset of set B, A ⊆ B, or A is contained in B if every element of A is also an element of B
- If x ∈ A then x ∈ B
- However, if A is not a subset of set B, then A ⊈ B
- Example: the set of all odd positive integers less than 10 is a subset of all positive integers less than 100
- If A is a subset of B, and A ≠ B, then A is the proper subset of B, A ⊂ B
Cardinality of Sets
- A set A is called finite if it has n distinct elements, where n ∈ N
- n is the cardinality of A, denoted by |A|
- Example: A = { 1, 2, 3, 4, 5}, therefore |A| = 5
- Example: S = {x | x is a letter in the English alphabet }. |S| = 26
- | Ø | = 0
Power Set
- If S is a set, then the set of all subsets of S is called the power set of S, denoted by P(S)
- Example: if A = {1, 2, 3}, then P(A) = { Ø, { 1 }, { 2 }, { 3 }, { 1, 2 }, {1,3}, {2,3}, {1, 2, 3} }
- Example: if S = { Ø }, then P(S) = { Ø, { Ø } }
- If |A| = n, then | P(A)| = 2^n
Cartesian Products
- Ordered n-tuples provide a structure to represent ordered collections
- The ordered n-tuple (a1, a2, ..., an) is the ordered collection that has a1 as its first element, a2 as its second element, …, and an as its nth element
- Given a collection of sets A1, A2,..., An, the Cartesian product is the set of n-tuples (a1, a2,..., an), such that a(i) belongs to A(i) for i = 1, 2,..., n.
- The notation for this is A1 x A2 x ... x An = = { (a1, a2,..., an) | a(i) ∈ A(i), for i = 1,2,...,n }
- Another key identity is A x B = set of all ordered pairs (a,b) where a ∈ A and b ∈ B = { (a, b) | a ∈ A and b ∈ B }
Set Operations
- Union: the set consisting of all elements that belongs to A or B or both, denoted by A ∪ B
- A ∪ B = { x | x ∈ A or x ∈ B }
- Intersection: set of all elements that belong to both A and B, denoted by A ∩ B
- A ∩ B = { x | x ∈ A and x ∈ B }
- Complement: set of all elements that belong to A but not to B, denoted by A – B (difference of A and B).
- A – B = { x | x ∈ A and x ∉ B }
- The complement of A, is A = { x | x ∉ A } where U is the universal set containing A
- Symmetric Difference: set of all elements that belong to A or to B, but not to both A and B, denoted by A ⊕ B
- A ⊕ B = { x | (x∈A and x∉B) or (x∈B and x∉A) }
Set Identities
- Identity Laws: A ∪ Ø = A, A ∩ U = A
- Domination Laws: A ∪ U = U, A ∩ Ø = Ø
- Idempotent Laws: A ∪ A = A, A ∩ A = A
- Complementation Law: (A) = A
- Commutative Laws: A ∪ B = B ∪ A, A ∩ B = B ∩ A
- Associative Laws: (A ∪ B) ∪ C = A ∪ (B ∪ C), (A ∩ B) ∩ C = A ∩ (B ∩ C)
- Distributive Laws: A ∪ (B ∩ C) = (A ∪ B) ∩ (A ∪ C), A ∩ (B ∪ C) = (A ∩ B) ∪ (A ∩ C)
- De Morgan's Laws: (A ∪ B) = A ∩ B, (A ∩ B) = A ∪ B
- Absorption Laws: A ∪ (A ∩ B) = A, A ∩ (A ∪ B) = A
- Complement Laws: A ∪ A = U, A ∩ A = Ø, Ū = Ø, Ø = U
Membership Tables
- Set identities can also be proved using membership tables
- 1: element is in a set
- 0: element is not in a set
Counting Principle of Finite Sets
- If A and B are disjoint sets, then A ∪ B is finite and |A ∪ B| = |A| + |B|
- If A and B are finite sets, then A ∪ B and A ∩ B are finite and |A ∪ B| = |A| + |B| - |A ∩ B|
- If A, B and C are finite sets, then so is A ∪ B ∪ C, and |A ∪ B ∪ C| = |A| + |B| + |C| - |A ∩ B| - |A ∩ C| - |B ∩ C| + |A ∩ B ∩ C|
Computer Representation of Sets
- One way to represent sets in a computer is using bit strings
- First, specify an arbitrary ordering of the elements of U- U is finite
- Finding complements of sets, unions, intersections, and differences of sets is easy using bit strings
- To get the union and intersection of two sets, perform bitwise Boolean OR and AND, respectively
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