Elements of Set Theory - CM0832(complementary slides)
Andrés Sicard-Ramírez
Universidad EAFIT
Semester 2017-2
Administrative InformationCourse web pagehttp://www1.eafit.edu.co/asr/courses/set-theory-CM0832/
Exams, bibliography, etc.See course web page.
Introduction 2/144
NotationLogical constants∧ (and) conjunction∨ (or) inclusive∗ disjunction⇒ (if , then ) implication¬ (not) negation⇔ (if and only if, iff) bi-implication, equivalence⊥ (falsity) bottom, falsum∀𝑥 (for every 𝑥) universal quantifier∃𝑥 (there exists a 𝑥) existential quantifier
SetsSets will be denote by lowercase letters (𝑎, 𝑏, …), uppercase letters(𝐴, 𝐵, …), script letters (𝒜, ℬ, …) and Greek letters (𝛼, 𝛽, …).
∗One or the other or both.Introduction 3/144
Origins
Georg Cantor (1845 – 1918)
Introduction 4/144
Origins
“Set theory was invented by Georg Can-tor…It was however Cantor who realizedthe significance of one-to-one functionsbetween sets and introduced the notionof cardinality of a set.” (p. 15)
Introduction 5/144
Origins
“Set theory was born on that Decem-ber 1873 day when Cantor establishedthat the reals are uncountable, i.e.there is no one-to-one correspond-ence between the reals and the naturalnumbers.” (p. XII)
Introduction 6/144
Naive Set TheoryRemarkCantor set theory is also called naive set theory.
Cantor’s set definitionCantor (1895, p. 1):
“Unter einer Menge verstehen wir jede Zusammenfassung M vonbestimmten wohlunterschiedenen Objekten m unserer Anschauungoder unseres Denkens (welche die Elemente von M genannt wer-den) zu einem Ganzen.”
Translation (Hrbacek and Jech 1999, p. 1):“A set is a collection into a whole of definite, distinct objectsof our intuition or our thought. The objects are called elements(members) of the set.”
Introduction 7/144
Naive Set TheoryRemarkCantor set theory is also called naive set theory.
Cantor’s set definitionCantor (1895, p. 1):
“Unter einer Menge verstehen wir jede Zusammenfassung M vonbestimmten wohlunterschiedenen Objekten m unserer Anschauungoder unseres Denkens (welche die Elemente von M genannt wer-den) zu einem Ganzen.”
Translation (Hrbacek and Jech 1999, p. 1):“A set is a collection into a whole of definite, distinct objectsof our intuition or our thought. The objects are called elements(members) of the set.”
Introduction 8/144
Naive Set Theory
Membership relation (a binary relation)𝑡 ∈ 𝐴 means that 𝑡 is a member of 𝐴.The expression 𝑡 ∉ 𝐴 is an abbreviation of ¬(𝑡 ∈ 𝐴).
Principle of extensionality, empty set, pair set, union and intersection,subset and power setSee whiteboard.
Introduction 9/144
Naive Set Theory
Membership relation (a binary relation)𝑡 ∈ 𝐴 means that 𝑡 is a member of 𝐴.The expression 𝑡 ∉ 𝐴 is an abbreviation of ¬(𝑡 ∈ 𝐴).
Principle of extensionality, empty set, pair set, union and intersection,subset and power setSee whiteboard.
Introduction 10/144
Naive Set TheoryProblemImplicit use of properties of sets.
Example (axiom of choice)
Illustration of the axiom.∗
The proof that a real function 𝑓 is continuous at a point 𝑝 if and only if 𝑓is sequentially continuous at 𝑝 [Heine 1872] requires of the axiom ofchoice. See (Moore 1982, p. 14) or (Hrbacek and Jech 1999, pp. 145-6).
∗Figure from https://commons.wikimedia.org/w/index.php?curid=48219447.Introduction 11/144
Naive Set TheoryProblemImplicit use of properties of sets.
Example (axiom of choice)
Illustration of the axiom.∗
The proof that a real function 𝑓 is continuous at a point 𝑝 if and only if 𝑓is sequentially continuous at 𝑝 [Heine 1872] requires of the axiom ofchoice. See (Moore 1982, p. 14) or (Hrbacek and Jech 1999, pp. 145-6).
∗Figure from https://commons.wikimedia.org/w/index.php?curid=48219447.Introduction 12/144
Naive Set TheoryProblemImplicit use of properties of sets.
Example (axiom of choice)
Illustration of the axiom.∗
The proof that a real function 𝑓 is continuous at a point 𝑝 if and only if 𝑓is sequentially continuous at 𝑝 [Heine 1872] requires of the axiom ofchoice. See (Moore 1982, p. 14) or (Hrbacek and Jech 1999, pp. 145-6).
∗Figure from https://commons.wikimedia.org/w/index.php?curid=48219447.Introduction 13/144
Naive Set TheoryProblemToo general method of abstraction.
Example (Russell’s paradox)See whiteboard.
Introduction 14/144
Naive Set TheoryProblemToo general method of abstraction.
Example (Russell’s paradox)See whiteboard.
Introduction 15/144
Russell’s Paradox
Gottlob Frege(1848 – 1925)
Bertrand Russell(1872 – 1970)
Introduction 16/144
Letter from Russell to van Heijenoort∗
Penrhyndeudraeth, 23 November 1962Dear Professor van Heijenoort,As I think about acts of integrity and grace, I realise there is nothing in myknowledge to compare with Frege’s dedication to truth. His entire life’swork was on the verge of completion, much of his work had been ignoredto the benefit of men infinitely less capable, his second volume was aboutto be published, and upon finding that his fundamental assumption was inerror, he responded with intellectual pleasure clearly submerging anyfeelings of personal disappointment. It was almost superhuman and atelling indication of that of which men are capable if their dedication is tocreative work and knowledge instead of cruder efforts to dominate and beknown.
Yours sincerelyBertrand Russell
∗van Heijenoort (1967, p. 127).Introduction 17/144
Naive Set TheoryProblemToo general method of abstraction.
ExerciseWhich is the paradox called Berry paradox?
Introduction 18/144
Informally Building Sets∗
Sets-An Informal View 7
2. Show that no two o~e three sets 0, {0}, and {{0}} are equal to each other.
3. Show that if B S; C, then f!J B S; f!Jc.
4. Assume that x and yare members ofa set B. Show that {{x}, {x, y}} E f!Jf!JB.
SETS-AN INFORMAL VIEW
We are about to present a somewhat vague description of how sets are obtained. (The description will be repeated much later in precise form.) None of our later work will actually depend on this informal description, but we hope it will illuminate the motivation behind some of the things we will do.
IX
w
o
Fig.2. Vo is the set A of atoms.
First we gather together all those things that are not themselves sets but that we want to have as members of sets. Call such things atoms. For example, if we want to be able to speak of the set of all two-headed coins, then we must include all such coins in our collection of atoms. Let A be the set of all atoms; it is the first set in our description.
We now proceed to build up a hierarchy
Vos; VlS; V2
s;",
of sets. At the bottom level (in a vertical arrangement as in Fig. 2) we take Vo = A, the set of atoms. the next level will also contain all sets of atoms:
Vl = Vo u f!J Vo = A u f!J A.
𝑉0 = 𝐴 (set of atoms) 𝑉𝜔 = 𝑉0 ∪ 𝑉1 ∪ ⋯ 𝑉𝛼+1 = 𝑉𝛼 ∪ 𝒫𝑉𝛼𝑉𝑛+1 = 𝑉𝑛 ∪ 𝒫𝑉𝑛 𝑉𝜔+1 = 𝑉𝜔 ∪ 𝒫𝑉𝜔∗Fig. 2 (Enderton 1977).
Introduction 19/144
Informally Building Sets∗
Sets-An Informal View 9
is that the atoms serve no mathematically necessary purpose, so we banish them; we take A = 0. In so doing, we lose the ability to form sets of flowers or sets of people. But this is no cause for concern; we do not need set theory to talk about people and we do not need people in our set theory. But we definitely do want to have sets of numbers, e.g., {2, 3 + in}. Numbers do not appear at first glance to be sets. But as we shall discover (in Chapters 4 and 5), we can find sets that serve perfectly well as numbers.
Our theory then will ignore all objects that are not sets (as interesting and real as such objects may be). Instead we will concentrate just on "pure" sets that can be constructed without the use of such external objects. In
1 v. 1
v.,
I Fig. 3. The ordinals are the backbone of the universe.
particular, any member of one of our sets will itself be a set, and each of its members, if any, will be a set, and so forth. (This does not produce an infinite regress, because we stop when we reach 0.)
Now that we have banished atoms, the picture becomes narrower (Fig. 3). The construction is also simplified. We have defined ~+1 to be J<. u £!l>V . Now it turns out that this is the same as A u £!l>V (see Exercise
a a 6). With A = 0, we have simply ~+1 = £!l>~.
Exercises
5. Define the rank of a set c to be the least IX such that c £ V . Compute the rank of {{0}}. Compute the rank of {0, {0}, {0, {0}}}. a
6. We have stated that ~ + 1 = A u £!l> ~. Prove this at least for IX < 3.
7. List all the members of V3
• List all the members of V4
• (It is to be assumed here that there are no atoms.)
The ordinal numbers are the backbone of the universe.
𝑉0 = ∅ (no atoms) 𝑉𝛼+1 = 𝒫𝑉𝛼∗Fig. 3 (Enderton 1977).
Introduction 20/144
ClassesInformal descriptionA set is a class, but some classes are too large to be a sets.
ExampleThe collection of all sets.
RemarkA class A is a set if 𝐴 ⊆ 𝑉𝛼 (𝐴 ∈ 𝑉𝛼+1) for some 𝛼.
Introduction 21/144
Axiomatic MethodSome features
Axioms: Explicitly list of assumptions
Theorems: Logical consequence of the axiomsProperty of set theory: It should be an axiom or a theorem
Introduction 22/144
Axiomatic MethodSome features
Axioms: Explicitly list of assumptionsTheorems: Logical consequence of the axioms
Property of set theory: It should be an axiom or a theorem
Introduction 23/144
Axiomatic MethodSome features
Axioms: Explicitly list of assumptionsTheorems: Logical consequence of the axiomsProperty of set theory: It should be an axiom or a theorem
Introduction 24/144
Axiomatic MethodAxiomatic set theory as a fundational system for mathematics
A common affirmation is that “mathematics can be embedded into settheory”.
“our axioms provide a sufficient collection of assumptions for the devel-opment of the whole of mathematics—a remarkable fact.” (Enderton1977, p. 11)
“Experience has shown that practically all notions used in contempor-ary mathematics can be defined, and their mathematical propertiesderived, in this axiomatic system. In this sense, the axiomatic settheory serves as a satisfactory foundations for the other branches ofmathematics.” (Hrbacek and Jech 1999, p. 3)
Introduction 25/144
Axiomatic MethodSome axiomatic systems
Zermelo-Fraenkel set theory with choice (ZFC)von Neumann–Bernays–Gödel set theoryTarski–Grothendieck set theory
Introduction 26/144
Axiomatic MethodFirst-Order Theories∗
“The adjective ‘first-order’ is used to distinguish the languages…from thosein which are predicates having other predicates or functions as arguments,or quantification over functions or predicates, or both.” (Mendelson 1997,p. 56)
Primitive notionsWe only need two primitive notions, “set” and “member”.
∗For an introduction to first-order languages and first-order theories see, for example,(Hamilton 1978) or (Mendelson 1997).
Introduction 27/144
Axiomatic MethodFirst-Order Theories∗
“The adjective ‘first-order’ is used to distinguish the languages…from thosein which are predicates having other predicates or functions as arguments,or quantification over functions or predicates, or both.” (Mendelson 1997,p. 56)
Primitive notionsWe only need two primitive notions, “set” and “member”.
∗For an introduction to first-order languages and first-order theories see, for example,(Hamilton 1978) or (Mendelson 1997).
Introduction 28/144
Axioms and Operations
Extensionality AxiomExtensionality axiomIf two sets have exactly the same members, then they are equal, that is,
∀𝐴 ∀𝐵 [∀𝑥 (𝑥 ∈ 𝐴 ⇔ 𝑥 ∈ 𝐵) ⇒ 𝐴 = 𝐵].
RemarkA set is pure if its members are also sets.
QuestionHave we any set? No, we haven’t.
Axioms and Operations 30/144
Extensionality AxiomExtensionality axiomIf two sets have exactly the same members, then they are equal, that is,
∀𝐴 ∀𝐵 [∀𝑥 (𝑥 ∈ 𝐴 ⇔ 𝑥 ∈ 𝐵) ⇒ 𝐴 = 𝐵].RemarkA set is pure if its members are also sets.
QuestionHave we any set? No, we haven’t.
Axioms and Operations 31/144
Extensionality AxiomExtensionality axiomIf two sets have exactly the same members, then they are equal, that is,
∀𝐴 ∀𝐵 [∀𝑥 (𝑥 ∈ 𝐴 ⇔ 𝑥 ∈ 𝐵) ⇒ 𝐴 = 𝐵].RemarkA set is pure if its members are also sets.
QuestionHave we any set? No, we haven’t.
Axioms and Operations 32/144
Some Axioms for Building Sets
Empty (existence) axiomThere is a set having no members, that is,
∃𝐵 ∀𝑥 (𝑥 ∉ 𝐵).
Pairing axiomFor any sets 𝑢 and 𝑣, there is a set having as members just 𝑢 and 𝑣, thatis,
∀𝑎 ∀𝑏 ∃𝐶 ∀𝑥 (𝑥 ∈ 𝐶 ⇔ 𝑥 = 𝑎 ∨ 𝑥 = 𝑏).
Axioms and Operations 33/144
Some Axioms for Building Sets
Empty (existence) axiomThere is a set having no members, that is,
∃𝐵 ∀𝑥 (𝑥 ∉ 𝐵).Pairing axiomFor any sets 𝑢 and 𝑣, there is a set having as members just 𝑢 and 𝑣, thatis,
∀𝑎 ∀𝑏 ∃𝐶 ∀𝑥 (𝑥 ∈ 𝐶 ⇔ 𝑥 = 𝑎 ∨ 𝑥 = 𝑏).
Axioms and Operations 34/144
Some Axioms for Building Sets
Union axiom (first version)For any sets 𝑎 and 𝑏, there is a set whose members are those sets belongingeither to 𝑎 or to 𝑏 (or both), that is,
∀𝑎 ∀𝑏 ∃𝐵 ∀𝑥 (𝑥 ∈ 𝐵 ⇔ 𝑥 ∈ 𝑎 ∨ 𝑥 ∈ 𝑏).Power set axiomFor any set 𝑎, there is a set whose members are exactly the subsets of 𝑎,that is,
∀𝑎 ∃𝐵 ∀𝑥 (𝑥 ∈ 𝐵 ⇔ 𝑥 ⊆ 𝑎),where
𝑢 ⊆ 𝑣 ⇔ ∀𝑡 (𝑡 ∈ 𝑢 ⇒ 𝑡 ∈ 𝑣).
Axioms and Operations 35/144
Subset Axiom SchemeIntroductionSee whiteboard.
Abstractions from the empty, pairing, union and power set axiomsLet 𝑎, 𝑏, 𝑢 and 𝑣 be sets, then
∅ = {𝑥 ∣ 𝑥 ≠ 𝑥},{𝑢, 𝑣} = {𝑥 ∣ 𝑥 = 𝑢 ∨ 𝑥 = 𝑣},𝑎 ∪ 𝑏 = {𝑥 ∣ 𝑥 ∈ 𝑎 ∨ 𝑥 ∈ 𝑏},
𝒫𝑎 = {𝑥 ∣ 𝑥 ⊆ 𝑎}.
Axioms and Operations 36/144
Subset Axiom SchemeIntroductionSee whiteboard.
Abstractions from the empty, pairing, union and power set axiomsLet 𝑎, 𝑏, 𝑢 and 𝑣 be sets, then
∅ = {𝑥 ∣ 𝑥 ≠ 𝑥},{𝑢, 𝑣} = {𝑥 ∣ 𝑥 = 𝑢 ∨ 𝑥 = 𝑣},𝑎 ∪ 𝑏 = {𝑥 ∣ 𝑥 ∈ 𝑎 ∨ 𝑥 ∈ 𝑏},
𝒫𝑎 = {𝑥 ∣ 𝑥 ⊆ 𝑎}.
Axioms and Operations 37/144
Subset Axiom Scheme
Subset axiom scheme (axiom scheme of comprehension/separation)For each formula 𝜑, not containing 𝐵, the following is an axiom:
∀𝑡1 ⋯ ∀𝑡𝑘 ∀𝑐 ∃𝐵 ∀𝑥 (𝑥 ∈ 𝐵 ⇔ 𝑥 ∈ 𝑐 ∧ 𝜑).
RemarkWe stated an axiom scheme!
Abstraction from the subset axiom scheme{𝑥 ∈ 𝑐 ∣ 𝜑} is the set of all 𝑥 ∈ 𝑐 satisfying the property 𝜑.
Axioms and Operations 38/144
Subset Axiom Scheme
Subset axiom scheme (axiom scheme of comprehension/separation)For each formula 𝜑, not containing 𝐵, the following is an axiom:
∀𝑡1 ⋯ ∀𝑡𝑘 ∀𝑐 ∃𝐵 ∀𝑥 (𝑥 ∈ 𝐵 ⇔ 𝑥 ∈ 𝑐 ∧ 𝜑).RemarkWe stated an axiom scheme!
Abstraction from the subset axiom scheme{𝑥 ∈ 𝑐 ∣ 𝜑} is the set of all 𝑥 ∈ 𝑐 satisfying the property 𝜑.
Axioms and Operations 39/144
Subset Axiom Scheme
Subset axiom scheme (axiom scheme of comprehension/separation)For each formula 𝜑, not containing 𝐵, the following is an axiom:
∀𝑡1 ⋯ ∀𝑡𝑘 ∀𝑐 ∃𝐵 ∀𝑥 (𝑥 ∈ 𝐵 ⇔ 𝑥 ∈ 𝑐 ∧ 𝜑).RemarkWe stated an axiom scheme!
Abstraction from the subset axiom scheme{𝑥 ∈ 𝑐 ∣ 𝜑} is the set of all 𝑥 ∈ 𝑐 satisfying the property 𝜑.
Axioms and Operations 40/144
Subset Axiom Scheme
Theorem 2A (Enderton 1977)There is no set to which every set belongs.
ExerciseWhy does the subset axiom scheme avoid the Berry paradox?
Axioms and Operations 41/144
Subset Axiom Scheme
Theorem 2A (Enderton 1977)There is no set to which every set belongs.
ExerciseWhy does the subset axiom scheme avoid the Berry paradox?
Axioms and Operations 42/144
Arbitrary Unions and IntersectionsDefinitionLet 𝐴 be a set. The union ⋃ 𝐴 of 𝐴 is defined by
⋃ 𝐴 = {𝑥 ∣ ∃𝑏 (𝑏 ∈ 𝐴 ∧ 𝑥 ∈ 𝑏}.Examples
𝑎 ∪ 𝑏 = ⋃{𝑎, 𝑏},⋃{𝑎} = 𝑎,
⋃ ∅ = ∅.
Union axiom (final version)For any set 𝐴, there exists a set 𝐵 whose elements are exactly the membersof the members of 𝐴, that is,
∀𝐴 ∃𝐵 ∀𝑥 [𝑥 ∈ 𝐵 ⇔ ∃𝑏 (𝑏 ∈ 𝐴 ∧ 𝑥 ∈ 𝑏)].
Axioms and Operations 43/144
Arbitrary Unions and IntersectionsDefinitionLet 𝐴 be a set. The union ⋃ 𝐴 of 𝐴 is defined by
⋃ 𝐴 = {𝑥 ∣ ∃𝑏 (𝑏 ∈ 𝐴 ∧ 𝑥 ∈ 𝑏}.Examples
𝑎 ∪ 𝑏 = ⋃{𝑎, 𝑏},⋃{𝑎} = 𝑎,
⋃ ∅ = ∅.
Union axiom (final version)For any set 𝐴, there exists a set 𝐵 whose elements are exactly the membersof the members of 𝐴, that is,
∀𝐴 ∃𝐵 ∀𝑥 [𝑥 ∈ 𝐵 ⇔ ∃𝑏 (𝑏 ∈ 𝐴 ∧ 𝑥 ∈ 𝑏)].
Axioms and Operations 44/144
Arbitrary Unions and Intersections
Theorem 2B (Enderton 1977)For any nonempty set 𝐴, there exists a unique set 𝐵 such that for any 𝑥,
𝑥 ∈ 𝐵 ⇔ 𝑥 belongs to every member of 𝐴.
CharacterisationLet 𝐴 be a nonempty set. The intersecction ⋂ 𝐴 of 𝐴 can be character-ised by
⋂ 𝐴 = {𝑥 ∣ ∀𝑏 (𝑏 ∈ 𝐴 ⇒ 𝑥 ∈ 𝑏}, for𝐴 ≠ ∅.
Axioms and Operations 45/144
Arbitrary Unions and Intersections
Theorem 2B (Enderton 1977)For any nonempty set 𝐴, there exists a unique set 𝐵 such that for any 𝑥,
𝑥 ∈ 𝐵 ⇔ 𝑥 belongs to every member of 𝐴.
CharacterisationLet 𝐴 be a nonempty set. The intersecction ⋂ 𝐴 of 𝐴 can be character-ised by
⋂ 𝐴 = {𝑥 ∣ ∀𝑏 (𝑏 ∈ 𝐴 ⇒ 𝑥 ∈ 𝑏}, for𝐴 ≠ ∅.
Axioms and Operations 46/144
Algebra of Sets
Exercise (Enderton (1977), Exercise 2.18)Assume that 𝐴 and 𝐵 are subsets of 𝑆. List all of the different sets thatcan be made from these three by use of the binary operations ∪, ∩, and −.
The Venn diagram shows four possible regions for shading, that is, wehave 24 different sets given by∅, 𝐴, 𝐵, 𝑆, 𝐴 ∪ 𝐵, 𝐴 ∩ 𝐵, 𝐴 − 𝐵, 𝐵 − 𝐴, 𝐴 + 𝐵, 𝑆 − 𝐴, 𝑆 − 𝐵,𝑆 − (𝐴 ∪ 𝐵), 𝑆 − (𝐴 ∩ 𝐵), 𝑆 − (𝐴 − 𝐵), 𝑆 − (𝐵 − 𝐴) and 𝑆 − (𝐴 + 𝐵),where the binary operation + is the symmetric difference defined by
𝐴 + 𝐵 = (𝐴 − 𝐵) ∪ (𝐵 − 𝐴)= (𝐴 ∪ 𝐵) − (𝐴 ∩ 𝐵).
Axioms and Operations 47/144
Algebra of Sets
Exercise (Enderton (1977), Exercise 2.18)Assume that 𝐴 and 𝐵 are subsets of 𝑆. List all of the different sets thatcan be made from these three by use of the binary operations ∪, ∩, and −.The Venn diagram shows four possible regions for shading, that is, wehave 24 different sets given by∅, 𝐴, 𝐵, 𝑆, 𝐴 ∪ 𝐵, 𝐴 ∩ 𝐵, 𝐴 − 𝐵, 𝐵 − 𝐴, 𝐴 + 𝐵, 𝑆 − 𝐴, 𝑆 − 𝐵,𝑆 − (𝐴 ∪ 𝐵), 𝑆 − (𝐴 ∩ 𝐵), 𝑆 − (𝐴 − 𝐵), 𝑆 − (𝐵 − 𝐴) and 𝑆 − (𝐴 + 𝐵),where the binary operation + is the symmetric difference defined by
𝐴 + 𝐵 = (𝐴 − 𝐵) ∪ (𝐵 − 𝐴)= (𝐴 ∪ 𝐵) − (𝐴 ∩ 𝐵).
Axioms and Operations 48/144
Relations and Functions
Ordered PairsDefinitionLet 𝑎 and 𝑏 be sets. The order pair ⟨𝑎, 𝑏⟩ should be a set such that
⟨𝑎, 𝑏⟩ = ⟨𝑐, 𝑑⟩ ⇔ 𝑎 = 𝑐 ∧ 𝑏 = 𝑑.
We can define the order pairs via Kuratowski’s definition, that is, ⟨𝑎, 𝑏⟩ isthe set {{𝑎}, {𝑎, 𝑏}}.
ExerciseTo give a different definition of order pairs.
Relations and Functions 50/144
Cartesian ProductDefinitionLet 𝐴 and 𝐵 be sets. The Cartesian product of 𝐴 and 𝐵, denoted by 𝐴×𝐵,is defined by
𝐴 × 𝐵 = {⟨𝑥, 𝑦⟩ ∣ 𝑥 ∈ 𝐴 ∧ 𝑦 ∈ 𝐵}.
Is 𝐴 × 𝐵 a set?Yes! We can define it via the subset axiom scheme by
𝐴 × 𝐵 = {⟨𝑥, 𝑦⟩ ∈ 𝒫𝒫(𝐴 ∪ 𝐵) ∣ 𝑥 ∈ 𝐴 ∧ 𝑦 ∈ 𝐵}.
Relations and Functions 51/144
RelationsDefinitionA relation is a set of ordered pairs.
ExamplesSee whiteboard.
Relations and Functions 52/144
RelationsDefinitionLet 𝑅 be a relation. We define the domain, the range and the field of 𝑅 by
dom 𝑅 = {𝑥 ∣ ∃𝑦(⟨𝑥, 𝑦⟩ ∈ 𝑅)},ran 𝑅 = {𝑦 ∣ ∃𝑥(⟨𝑥, 𝑦⟩ ∈ 𝑅)},fld 𝑅 = dom 𝑅 ∪ ran 𝑅.
Are dom 𝑅 and ran 𝑅 sets?Yes! We can define them via the subset axiom scheme by
dom 𝑅 = {𝑥 ∈ ⋃ ⋃ 𝑅 ∣ ∃𝑦(⟨𝑥, 𝑦⟩ ∈ 𝑅)},ran 𝑅 = {𝑦 ∈ ⋃ ⋃ 𝑅 ∣ ∃𝑥(⟨𝑥, 𝑦⟩ ∈ 𝑅)}.
Relations and Functions 53/144
RelationsDefinitionLet 𝑅 be a relation. We define the domain, the range and the field of 𝑅 by
dom 𝑅 = {𝑥 ∣ ∃𝑦(⟨𝑥, 𝑦⟩ ∈ 𝑅)},ran 𝑅 = {𝑦 ∣ ∃𝑥(⟨𝑥, 𝑦⟩ ∈ 𝑅)},fld 𝑅 = dom 𝑅 ∪ ran 𝑅.
Are dom 𝑅 and ran 𝑅 sets?Yes! We can define them via the subset axiom scheme by
dom 𝑅 = {𝑥 ∈ ⋃ ⋃ 𝑅 ∣ ∃𝑦(⟨𝑥, 𝑦⟩ ∈ 𝑅)},ran 𝑅 = {𝑦 ∈ ⋃ ⋃ 𝑅 ∣ ∃𝑥(⟨𝑥, 𝑦⟩ ∈ 𝑅)}.
Relations and Functions 54/144
FunctionsDefinitionA function is a relation 𝐹 such that for each 𝑥 in dom 𝐹 there is only one 𝑦such that 𝑥𝐹𝑦.
Exercise (Enderton (1977), Exercise 3.11)Prove the following version (for functions) of the extensionality principle:Assume that 𝐹 and 𝐺 are functions, dom 𝐹 = dom 𝐺, and 𝐹(𝑥) = 𝐺(𝑥)for all 𝑥 in the common domain. Then 𝐹 = 𝐺.
Relations and Functions 55/144
FunctionsDefinitionA function is a relation 𝐹 such that for each 𝑥 in dom 𝐹 there is only one 𝑦such that 𝑥𝐹𝑦.
Exercise (Enderton (1977), Exercise 3.11)Prove the following version (for functions) of the extensionality principle:Assume that 𝐹 and 𝐺 are functions, dom 𝐹 = dom 𝐺, and 𝐹(𝑥) = 𝐺(𝑥)for all 𝑥 in the common domain. Then 𝐹 = 𝐺.
Relations and Functions 56/144
FunctionsDefinitionsLet 𝐴, 𝐹 and 𝐺 be sets. We define, the inverse of 𝐹 , the compositionof 𝐹 and 𝐺, the restriction of 𝐹 to 𝐴 and the image of 𝐴 under 𝐹 by
𝐹 −1 = {⟨𝑦, 𝑥⟩ ∣ 𝑥𝐹𝑦} (inverse of 𝐹 )
𝐹 ∘ 𝐺 = {⟨𝑥, 𝑦⟩ ∣ ∃𝑡 (𝑥𝐺𝑡 ∧ 𝑡𝐹𝑦)} (composition of 𝐹 and 𝐺)
𝐹 ↾ 𝐴 = {⟨𝑥, 𝑦⟩ ∣ 𝑥𝐹𝑦 ∧ 𝑥 ∈ 𝐴} (restriction of 𝐹 to 𝐴)
𝐹⟦𝐴⟧ = ran(𝐹 ↾ 𝐴) (image of 𝐴 under 𝐹 )= {𝑦 ∣ ∃𝑥(𝑥 ∈ 𝐴 ∧ 𝑥𝐹𝑦}
Relations and Functions 57/144
Functions
Exercise (Enderton (1977), Exercise 3.18)Let 𝑅 be the set
{⟨0, 1⟩, ⟨0, 2⟩, ⟨0, 3⟩, ⟨1, 2⟩, ⟨1, 3⟩, ⟨2, 3⟩}.
To find 𝑅 ∘ 𝑅, 𝑅 ↾ {1}, 𝑅−1 ↾ {1}, 𝑅⟦{1}⟧ and 𝑅−1⟦{1}⟧.
Exercise (Enderton (1977), Exercise 3.18)Let 𝐴, 𝐹 and 𝐺 be sets. Show that 𝐹 −1, 𝐹 ∘ 𝐺, 𝐹 ↾ 𝐴 and 𝐹⟦𝐴⟧ are sets.
Relations and Functions 58/144
Functions
Exercise (Enderton (1977), Exercise 3.18)Let 𝑅 be the set
{⟨0, 1⟩, ⟨0, 2⟩, ⟨0, 3⟩, ⟨1, 2⟩, ⟨1, 3⟩, ⟨2, 3⟩}.
To find 𝑅 ∘ 𝑅, 𝑅 ↾ {1}, 𝑅−1 ↾ {1}, 𝑅⟦{1}⟧ and 𝑅−1⟦{1}⟧.
Exercise (Enderton (1977), Exercise 3.18)Let 𝐴, 𝐹 and 𝐺 be sets. Show that 𝐹 −1, 𝐹 ∘ 𝐺, 𝐹 ↾ 𝐴 and 𝐹⟦𝐴⟧ are sets.
Relations and Functions 59/144
Axiom of Choice
Axiom of choice (first form)For any relation 𝑅 there is a function 𝐻 ⊆ 𝑅 with dom 𝐻 = dom 𝑅.
RemarkIs the axiom of choice accepted in constructive mathematics? (See, forexample, Martin-Löf (2006)).
Relations and Functions 60/144
Axiom of Choice
Axiom of choice (first form)For any relation 𝑅 there is a function 𝐻 ⊆ 𝑅 with dom 𝐻 = dom 𝑅.
RemarkIs the axiom of choice accepted in constructive mathematics? (See, forexample, Martin-Löf (2006)).
Relations and Functions 61/144
FunctionsDefinitionLet 𝐴 and 𝐵 be sets. We define the set of functions 𝐹 from 𝐴 into 𝐵 by
𝐴𝐵 = 𝐵𝐴 = {𝐹 ∣ 𝐹 ∶ 𝐴 → 𝐵}.
Examples{0, 1}𝜔
∅𝐴 = ∅ for 𝐴 ≠ ∅ (any function can have a non-empty domain and anempty range)𝐴∅ = {∅} for any set 𝐴 (∅ is the only function with an empty domain)
Is 𝐵𝐴 a set?Yes! We can define it via the subset axiom scheme by
𝐵𝐴 = {𝐹 ∈ 𝒫(𝐴 × 𝐵) ∣ 𝐹 ∶ 𝐴 → 𝐵}.
Relations and Functions 62/144
FunctionsDefinitionLet 𝐴 and 𝐵 be sets. We define the set of functions 𝐹 from 𝐴 into 𝐵 by
𝐴𝐵 = 𝐵𝐴 = {𝐹 ∣ 𝐹 ∶ 𝐴 → 𝐵}.Examples
{0, 1}𝜔
∅𝐴 = ∅ for 𝐴 ≠ ∅ (any function can have a non-empty domain and anempty range)𝐴∅ = {∅} for any set 𝐴 (∅ is the only function with an empty domain)
Is 𝐵𝐴 a set?Yes! We can define it via the subset axiom scheme by
𝐵𝐴 = {𝐹 ∈ 𝒫(𝐴 × 𝐵) ∣ 𝐹 ∶ 𝐴 → 𝐵}.
Relations and Functions 63/144
FunctionsDefinitionLet 𝐴 and 𝐵 be sets. We define the set of functions 𝐹 from 𝐴 into 𝐵 by
𝐴𝐵 = 𝐵𝐴 = {𝐹 ∣ 𝐹 ∶ 𝐴 → 𝐵}.Examples
{0, 1}𝜔
∅𝐴 = ∅ for 𝐴 ≠ ∅ (any function can have a non-empty domain and anempty range)
𝐴∅ = {∅} for any set 𝐴 (∅ is the only function with an empty domain)
Is 𝐵𝐴 a set?Yes! We can define it via the subset axiom scheme by
𝐵𝐴 = {𝐹 ∈ 𝒫(𝐴 × 𝐵) ∣ 𝐹 ∶ 𝐴 → 𝐵}.
Relations and Functions 64/144
FunctionsDefinitionLet 𝐴 and 𝐵 be sets. We define the set of functions 𝐹 from 𝐴 into 𝐵 by
𝐴𝐵 = 𝐵𝐴 = {𝐹 ∣ 𝐹 ∶ 𝐴 → 𝐵}.Examples
{0, 1}𝜔
∅𝐴 = ∅ for 𝐴 ≠ ∅ (any function can have a non-empty domain and anempty range)𝐴∅ = {∅} for any set 𝐴 (∅ is the only function with an empty domain)
Is 𝐵𝐴 a set?Yes! We can define it via the subset axiom scheme by
𝐵𝐴 = {𝐹 ∈ 𝒫(𝐴 × 𝐵) ∣ 𝐹 ∶ 𝐴 → 𝐵}.
Relations and Functions 65/144
FunctionsDefinitionLet 𝐴 and 𝐵 be sets. We define the set of functions 𝐹 from 𝐴 into 𝐵 by
𝐴𝐵 = 𝐵𝐴 = {𝐹 ∣ 𝐹 ∶ 𝐴 → 𝐵}.Examples
{0, 1}𝜔
∅𝐴 = ∅ for 𝐴 ≠ ∅ (any function can have a non-empty domain and anempty range)𝐴∅ = {∅} for any set 𝐴 (∅ is the only function with an empty domain)
Is 𝐵𝐴 a set?Yes! We can define it via the subset axiom scheme by
𝐵𝐴 = {𝐹 ∈ 𝒫(𝐴 × 𝐵) ∣ 𝐹 ∶ 𝐴 → 𝐵}.
Relations and Functions 66/144
Equivalence RelationsIdea∗
56 3. Relations and Functions
bricks.) This changes the picture (Fig. 12c); each box is now, in our mind, a single point. The set B of boxes is very different from the set A. In our example, B has only six members whereas A is infinite. (When we get around to defining "six" and "infinite" officially, we must certainly do it in a way that makes the preceding sentence true.)
The process of transforming a situation like Fig. 12a into Fig. 12c is common in abstract algebra and elsewhere in mathematics. And in Chapter 5 the process will be applied several times in the construction of the real numbers.
Suppose we now define a binary relation R on A as follows: For x and y in A,
xRy ¢> x and yare in the same little box.
• • •
• • •
(a) (b) (c)
Fig. 12. Partitioning a set into six little boxes.
Then we can easily see that R has the following three properties.
1. R is reflexive on A, by which we mean that xRx for all x E A. 2. R is symmetric, by which we mean that whenever xRy, then also
yRx. 3. R is transitive, by which we mean that whenever xRy and yRz,
then also xRz.
Definition R is an equivalence relation on A iff R is a binary relation on A that is reflexive on A, symmetric, and transitive.
Theorem 3M If R is a symmetric and transitive relation, then R is an equivalence relation on fld R.
Proof Any relation R is a binary relation on its field, since
R ~ dom R x ran R ~ fld R x fld R.
What we must show is that R is reflexive on fld R. We have
X E dom R => xRy
=> xRy & yRx
=> xRx
for some y
by symmetry
by transitivity,
and a similar calculation applies to points in ran R.
DefinitionsLet 𝑅 be a binary relation on a set 𝐴. The relation 𝑅 is
reflexive iff 𝑥𝑅𝑥 for all 𝑥 ∈ 𝐴,symmetric iff 𝑥𝑅𝑦 ⇒ 𝑦𝑅𝑥 for all 𝑥, 𝑦 ∈ 𝐴 andtransitive iff (𝑥𝑅𝑦 ∧ 𝑦𝑅𝑧) ⇒ 𝑥𝑅𝑧 for all 𝑥, 𝑦, 𝑧 ∈ 𝐴.
∗Fig. 12 (Enderton 1977).Relations and Functions 67/144
Equivalence RelationsIdea∗
56 3. Relations and Functions
bricks.) This changes the picture (Fig. 12c); each box is now, in our mind, a single point. The set B of boxes is very different from the set A. In our example, B has only six members whereas A is infinite. (When we get around to defining "six" and "infinite" officially, we must certainly do it in a way that makes the preceding sentence true.)
The process of transforming a situation like Fig. 12a into Fig. 12c is common in abstract algebra and elsewhere in mathematics. And in Chapter 5 the process will be applied several times in the construction of the real numbers.
Suppose we now define a binary relation R on A as follows: For x and y in A,
xRy ¢> x and yare in the same little box.
• • •
• • •
(a) (b) (c)
Fig. 12. Partitioning a set into six little boxes.
Then we can easily see that R has the following three properties.
1. R is reflexive on A, by which we mean that xRx for all x E A. 2. R is symmetric, by which we mean that whenever xRy, then also
yRx. 3. R is transitive, by which we mean that whenever xRy and yRz,
then also xRz.
Definition R is an equivalence relation on A iff R is a binary relation on A that is reflexive on A, symmetric, and transitive.
Theorem 3M If R is a symmetric and transitive relation, then R is an equivalence relation on fld R.
Proof Any relation R is a binary relation on its field, since
R ~ dom R x ran R ~ fld R x fld R.
What we must show is that R is reflexive on fld R. We have
X E dom R => xRy
=> xRy & yRx
=> xRx
for some y
by symmetry
by transitivity,
and a similar calculation applies to points in ran R.
DefinitionsLet 𝑅 be a binary relation on a set 𝐴. The relation 𝑅 is
reflexive iff 𝑥𝑅𝑥 for all 𝑥 ∈ 𝐴,
symmetric iff 𝑥𝑅𝑦 ⇒ 𝑦𝑅𝑥 for all 𝑥, 𝑦 ∈ 𝐴 andtransitive iff (𝑥𝑅𝑦 ∧ 𝑦𝑅𝑧) ⇒ 𝑥𝑅𝑧 for all 𝑥, 𝑦, 𝑧 ∈ 𝐴.
∗Fig. 12 (Enderton 1977).Relations and Functions 68/144
Equivalence RelationsIdea∗
56 3. Relations and Functions
bricks.) This changes the picture (Fig. 12c); each box is now, in our mind, a single point. The set B of boxes is very different from the set A. In our example, B has only six members whereas A is infinite. (When we get around to defining "six" and "infinite" officially, we must certainly do it in a way that makes the preceding sentence true.)
The process of transforming a situation like Fig. 12a into Fig. 12c is common in abstract algebra and elsewhere in mathematics. And in Chapter 5 the process will be applied several times in the construction of the real numbers.
Suppose we now define a binary relation R on A as follows: For x and y in A,
xRy ¢> x and yare in the same little box.
• • •
• • •
(a) (b) (c)
Fig. 12. Partitioning a set into six little boxes.
Then we can easily see that R has the following three properties.
1. R is reflexive on A, by which we mean that xRx for all x E A. 2. R is symmetric, by which we mean that whenever xRy, then also
yRx. 3. R is transitive, by which we mean that whenever xRy and yRz,
then also xRz.
Definition R is an equivalence relation on A iff R is a binary relation on A that is reflexive on A, symmetric, and transitive.
Theorem 3M If R is a symmetric and transitive relation, then R is an equivalence relation on fld R.
Proof Any relation R is a binary relation on its field, since
R ~ dom R x ran R ~ fld R x fld R.
What we must show is that R is reflexive on fld R. We have
X E dom R => xRy
=> xRy & yRx
=> xRx
for some y
by symmetry
by transitivity,
and a similar calculation applies to points in ran R.
DefinitionsLet 𝑅 be a binary relation on a set 𝐴. The relation 𝑅 is
reflexive iff 𝑥𝑅𝑥 for all 𝑥 ∈ 𝐴,symmetric iff 𝑥𝑅𝑦 ⇒ 𝑦𝑅𝑥 for all 𝑥, 𝑦 ∈ 𝐴 and
transitive iff (𝑥𝑅𝑦 ∧ 𝑦𝑅𝑧) ⇒ 𝑥𝑅𝑧 for all 𝑥, 𝑦, 𝑧 ∈ 𝐴.
∗Fig. 12 (Enderton 1977).Relations and Functions 69/144
Equivalence RelationsIdea∗
56 3. Relations and Functions
bricks.) This changes the picture (Fig. 12c); each box is now, in our mind, a single point. The set B of boxes is very different from the set A. In our example, B has only six members whereas A is infinite. (When we get around to defining "six" and "infinite" officially, we must certainly do it in a way that makes the preceding sentence true.)
The process of transforming a situation like Fig. 12a into Fig. 12c is common in abstract algebra and elsewhere in mathematics. And in Chapter 5 the process will be applied several times in the construction of the real numbers.
Suppose we now define a binary relation R on A as follows: For x and y in A,
xRy ¢> x and yare in the same little box.
• • •
• • •
(a) (b) (c)
Fig. 12. Partitioning a set into six little boxes.
Then we can easily see that R has the following three properties.
1. R is reflexive on A, by which we mean that xRx for all x E A. 2. R is symmetric, by which we mean that whenever xRy, then also
yRx. 3. R is transitive, by which we mean that whenever xRy and yRz,
then also xRz.
Definition R is an equivalence relation on A iff R is a binary relation on A that is reflexive on A, symmetric, and transitive.
Theorem 3M If R is a symmetric and transitive relation, then R is an equivalence relation on fld R.
Proof Any relation R is a binary relation on its field, since
R ~ dom R x ran R ~ fld R x fld R.
What we must show is that R is reflexive on fld R. We have
X E dom R => xRy
=> xRy & yRx
=> xRx
for some y
by symmetry
by transitivity,
and a similar calculation applies to points in ran R.
DefinitionsLet 𝑅 be a binary relation on a set 𝐴. The relation 𝑅 is
reflexive iff 𝑥𝑅𝑥 for all 𝑥 ∈ 𝐴,symmetric iff 𝑥𝑅𝑦 ⇒ 𝑦𝑅𝑥 for all 𝑥, 𝑦 ∈ 𝐴 andtransitive iff (𝑥𝑅𝑦 ∧ 𝑦𝑅𝑧) ⇒ 𝑥𝑅𝑧 for all 𝑥, 𝑦, 𝑧 ∈ 𝐴.
∗Fig. 12 (Enderton 1977).Relations and Functions 70/144
Equivalence RelationsDefinitionLet 𝑅 be a binary relation on a set 𝐴. The relation 𝑅 is a equivalencerelation iff 𝑅 is reflexive, symmetric and transitive.
QuestionsLet 𝐴 = {𝑎, 𝑒, 𝑖, 𝑜, 𝑢}. Is the equality relation on 𝐴 an equivalencerelation?Let 𝐴 ≠ ∅ be a set. Is the relation ∅ on 𝐴 an equivalence relation?Let 𝐴 be a set. Is the relation 𝐴 × 𝐴 an equivalence relations?Let 𝐴 be a singleton. It is possible to define an equivalence relationon 𝐴?
Relations and Functions 71/144
Equivalence RelationsDefinitionLet 𝑅 be a binary relation on a set 𝐴. The relation 𝑅 is a equivalencerelation iff 𝑅 is reflexive, symmetric and transitive.
QuestionsLet 𝐴 = {𝑎, 𝑒, 𝑖, 𝑜, 𝑢}. Is the equality relation on 𝐴 an equivalencerelation?
Let 𝐴 ≠ ∅ be a set. Is the relation ∅ on 𝐴 an equivalence relation?Let 𝐴 be a set. Is the relation 𝐴 × 𝐴 an equivalence relations?Let 𝐴 be a singleton. It is possible to define an equivalence relationon 𝐴?
Relations and Functions 72/144
Equivalence RelationsDefinitionLet 𝑅 be a binary relation on a set 𝐴. The relation 𝑅 is a equivalencerelation iff 𝑅 is reflexive, symmetric and transitive.
QuestionsLet 𝐴 = {𝑎, 𝑒, 𝑖, 𝑜, 𝑢}. Is the equality relation on 𝐴 an equivalencerelation?Let 𝐴 ≠ ∅ be a set. Is the relation ∅ on 𝐴 an equivalence relation?
Let 𝐴 be a set. Is the relation 𝐴 × 𝐴 an equivalence relations?Let 𝐴 be a singleton. It is possible to define an equivalence relationon 𝐴?
Relations and Functions 73/144
Equivalence RelationsDefinitionLet 𝑅 be a binary relation on a set 𝐴. The relation 𝑅 is a equivalencerelation iff 𝑅 is reflexive, symmetric and transitive.
QuestionsLet 𝐴 = {𝑎, 𝑒, 𝑖, 𝑜, 𝑢}. Is the equality relation on 𝐴 an equivalencerelation?Let 𝐴 ≠ ∅ be a set. Is the relation ∅ on 𝐴 an equivalence relation?Let 𝐴 be a set. Is the relation 𝐴 × 𝐴 an equivalence relations?
Let 𝐴 be a singleton. It is possible to define an equivalence relationon 𝐴?
Relations and Functions 74/144
Equivalence RelationsDefinitionLet 𝑅 be a binary relation on a set 𝐴. The relation 𝑅 is a equivalencerelation iff 𝑅 is reflexive, symmetric and transitive.
QuestionsLet 𝐴 = {𝑎, 𝑒, 𝑖, 𝑜, 𝑢}. Is the equality relation on 𝐴 an equivalencerelation?Let 𝐴 ≠ ∅ be a set. Is the relation ∅ on 𝐴 an equivalence relation?Let 𝐴 be a set. Is the relation 𝐴 × 𝐴 an equivalence relations?Let 𝐴 be a singleton. It is possible to define an equivalence relationon 𝐴?
Relations and Functions 75/144
Linear Ordering RelationsMotivationWhat means that 𝑅 is an ordering relation on a set 𝐴?
DefinitionLet 𝑅 be a binary relation on a set 𝐴. The relation 𝑅 satisfies trichotomyif exactly one of the three alternatives
𝑥𝑅𝑦, 𝑥 = 𝑦 or 𝑦𝑅𝑥
holds for all for all 𝑥, 𝑦 ∈ 𝐴.
Relations and Functions 76/144
Linear Ordering RelationsMotivationWhat means that 𝑅 is an ordering relation on a set 𝐴?
DefinitionLet 𝑅 be a binary relation on a set 𝐴. The relation 𝑅 satisfies trichotomyif exactly one of the three alternatives
𝑥𝑅𝑦, 𝑥 = 𝑦 or 𝑦𝑅𝑥
holds for all for all 𝑥, 𝑦 ∈ 𝐴.
Relations and Functions 77/144
Linear Ordering RelationsDefinitionLet 𝐴 be a set. A linear ordering (or total ordering) on 𝐴 is a binaryrelation 𝑅 on 𝐴 such that:
1. 𝑅 is transitive relation and2. 𝑅 satisfies trichotomy on 𝐴.
Example
0123
⋮
Relations and Functions 78/144
Linear Ordering RelationsDefinitionLet 𝐴 be a set. A linear ordering (or total ordering) on 𝐴 is a binaryrelation 𝑅 on 𝐴 such that:
1. 𝑅 is transitive relation and2. 𝑅 satisfies trichotomy on 𝐴.
Example
0123
⋮
Relations and Functions 79/144
Natural Numbers
Natural NumbersApproaches for introducing mathematical objects
axiomaticdefinitional
Definitional approach for introducing natural numbersWe shall define natural numbers in terms of setsWe shall prove the properties of natural numbers from properties ofsets
How to define natural numbers in terms of sets?
Natural Numbers 81/144
Natural NumbersApproaches for introducing mathematical objects
axiomaticdefinitional
Definitional approach for introducing natural numbersWe shall define natural numbers in terms of setsWe shall prove the properties of natural numbers from properties ofsets
How to define natural numbers in terms of sets?
Natural Numbers 82/144
Natural NumbersApproaches for introducing mathematical objects
axiomaticdefinitional
Definitional approach for introducing natural numbersWe shall define natural numbers in terms of setsWe shall prove the properties of natural numbers from properties ofsets
How to define natural numbers in terms of sets?
Natural Numbers 83/144
Natural Numbersvon Neumann’s constructionInformal idea: A natural number is the set of all smaller natural numbers
0 = ∅,1 = {0} = {∅},2 = {0, 1} = {∅, {∅}},3 = {0, 1, 2} = {∅, {∅}, {∅, {∅}}},
⋮
Some “extra” properties0 ∈ 1 ∈ 2 ∈ 3 ∈ ...
0 ⊆ 1 ⊆ 2 ⊆ 3 ⊆ ...
Natural Numbers 84/144
Natural Numbersvon Neumann’s constructionInformal idea: A natural number is the set of all smaller natural numbers
0 = ∅,1 = {0} = {∅},2 = {0, 1} = {∅, {∅}},3 = {0, 1, 2} = {∅, {∅}, {∅, {∅}}},
⋮Some “extra” properties
0 ∈ 1 ∈ 2 ∈ 3 ∈ ...
0 ⊆ 1 ⊆ 2 ⊆ 3 ⊆ ...
Natural Numbers 85/144
Natural NumbersA wrong impredicative definition
𝑛 = {0, 1, … , 𝑛 − 1}.“We cannot just say that a set 𝑛 is a natural number if its elements are allthe smaller natural numbers, because such a “definition” would involve thevery concept being defined.” (Hrbacek and Jech 1999, p. 40)
Natural Numbers 86/144
Natural NumbersDefinitionLet 𝑎 be a set. The successor of 𝑎 is
𝑎+ = 𝑎 ∪ {𝑎}.
Examples
0 = ∅,1 = ∅+,2 = ∅++,3 = ∅+++,⋮
Natural Numbers 87/144
Natural NumbersDefinitionLet 𝑎 be a set. The successor of 𝑎 is
𝑎+ = 𝑎 ∪ {𝑎}.
Examples
0 = ∅,1 = ∅+,2 = ∅++,3 = ∅+++,⋮
Natural Numbers 88/144
Natural NumbersDefinitionA set 𝐴 is called inductive iff
∅ ∈ 𝐴 andif 𝑎 ∈ 𝐴 then 𝑎+ ∈ 𝐴.
RemarkAn inductive set will be an infinite set.
QuestionAre there inductive sets?
Infinity axiomThere exists an inductive set, that is,
∃𝐴 [∅ ∈ 𝐴 ∧ ∀𝑎 (𝑎 ∈ 𝐴 ⇒ 𝑎+ ∈ 𝐴)].
Natural Numbers 89/144
Natural NumbersDefinitionA set 𝐴 is called inductive iff
∅ ∈ 𝐴 andif 𝑎 ∈ 𝐴 then 𝑎+ ∈ 𝐴.
RemarkAn inductive set will be an infinite set.
QuestionAre there inductive sets?
Infinity axiomThere exists an inductive set, that is,
∃𝐴 [∅ ∈ 𝐴 ∧ ∀𝑎 (𝑎 ∈ 𝐴 ⇒ 𝑎+ ∈ 𝐴)].
Natural Numbers 90/144
Natural NumbersDefinitionA set 𝐴 is called inductive iff
∅ ∈ 𝐴 andif 𝑎 ∈ 𝐴 then 𝑎+ ∈ 𝐴.
RemarkAn inductive set will be an infinite set.
QuestionAre there inductive sets?
Infinity axiomThere exists an inductive set, that is,
∃𝐴 [∅ ∈ 𝐴 ∧ ∀𝑎 (𝑎 ∈ 𝐴 ⇒ 𝑎+ ∈ 𝐴)].
Natural Numbers 91/144
Natural NumbersDefinitionA natural number is a set that belongs to every inductive set.
Theorem 4A (Enderton 1977)There is a set whose members are exactly the natural numbers.
DefinitionThe set of all natural numbers, denoted by 𝜔, is defined by
𝑥 ∈ 𝜔 ⇔ 𝑥 is a natural number.
Theorem 4B (Enderton 1977)The set 𝜔 is inductive, and is a subset of every other inductive set.
Natural Numbers 92/144
Natural NumbersDefinitionA natural number is a set that belongs to every inductive set.
Theorem 4A (Enderton 1977)There is a set whose members are exactly the natural numbers.
DefinitionThe set of all natural numbers, denoted by 𝜔, is defined by
𝑥 ∈ 𝜔 ⇔ 𝑥 is a natural number.
Theorem 4B (Enderton 1977)The set 𝜔 is inductive, and is a subset of every other inductive set.
Natural Numbers 93/144
Natural NumbersDefinitionA natural number is a set that belongs to every inductive set.
Theorem 4A (Enderton 1977)There is a set whose members are exactly the natural numbers.
DefinitionThe set of all natural numbers, denoted by 𝜔, is defined by
𝑥 ∈ 𝜔 ⇔ 𝑥 is a natural number.
Theorem 4B (Enderton 1977)The set 𝜔 is inductive, and is a subset of every other inductive set.
Natural Numbers 94/144
Natural NumbersDefinitionA natural number is a set that belongs to every inductive set.
Theorem 4A (Enderton 1977)There is a set whose members are exactly the natural numbers.
DefinitionThe set of all natural numbers, denoted by 𝜔, is defined by
𝑥 ∈ 𝜔 ⇔ 𝑥 is a natural number.
Theorem 4B (Enderton 1977)The set 𝜔 is inductive, and is a subset of every other inductive set.
Natural Numbers 95/144
Induction Principle for Natural Numbers
Induction principle for 𝜔 (one version)Any inductive subset of 𝜔 coincides with 𝜔 (Enderton 1977, p. 69).
Induction principle for 𝜔 (other version)Let 𝑃 (𝑥) be a property. Assume that
i) 𝑃 (0) holdsii) For all 𝑛 ∈ 𝜔, 𝑃(𝑛) implies 𝑃(𝑛+)
Then 𝑃 holds for all natural numbers 𝑛.
Proof.“This is an immediate consequence of our definition of 𝑤. The assump-tions i) and ii) simple say that the set 𝐴 = {𝑛 ∈ 𝜔 ∣ 𝑃(𝑛)} is induct-ive. 𝜔 ⊆ 𝐴 follows.” (Hrbacek and Jech 1999, p. 42)
Natural Numbers 96/144
Induction Principle for Natural Numbers
Induction principle for 𝜔 (one version)Any inductive subset of 𝜔 coincides with 𝜔 (Enderton 1977, p. 69).
Induction principle for 𝜔 (other version)Let 𝑃(𝑥) be a property. Assume that
i) 𝑃(0) holdsii) For all 𝑛 ∈ 𝜔, 𝑃(𝑛) implies 𝑃(𝑛+)
Then 𝑃 holds for all natural numbers 𝑛.
Proof.“This is an immediate consequence of our definition of 𝑤. The assump-tions i) and ii) simple say that the set 𝐴 = {𝑛 ∈ 𝜔 ∣ 𝑃(𝑛)} is induct-ive. 𝜔 ⊆ 𝐴 follows.” (Hrbacek and Jech 1999, p. 42)
Natural Numbers 97/144
Recursion on Natural Numbers
Recursion theorem on 𝜔 (Enderton 1977, p. 73)Let 𝐴 be a set, 𝑎 ∈ 𝐴 and 𝐹 ∶ 𝐴 → 𝐴. Then there exists a unique functionℎ ∶ 𝜔 → 𝐴 such that
ℎ(0) = 𝑎,and for every 𝑛 in 𝜔
ℎ(𝑛+) = 𝐹(ℎ(𝑛)).
Natural Numbers 98/144
Defining Natural Numbers as SetsRemarkSo far, we defined natural numbers on terms of sets. A different point ofview is stated by some authors (see, for example, Benacerraf (1965)).
Natural Numbers 99/144
Induction as Foundations“Thus inductive definibility is anotion intermediate in strengthbetween predicate and fullyimpredicative definability.” (Aczel1977, p. 780)
It would be interesting to formulate acoherent conceptual framework thatmade induction the principal notion.There are suggestions of this in theliterature, but the possibility has notyet been fully explored.” Ibid.
Natural Numbers 100/144
Cardinal Numbers
EquinumerosityRemarkA one-to-one function from 𝐴 onto 𝐵 is called a one-to-one correspond-ence between 𝐴 and 𝐵.
DefinitionA set 𝐴 is equinumerous to a set 𝐵, denoted 𝐴 ≈ 𝐵, iff there is a one-to-one correspondence between 𝐴 and 𝐵.
ExamplesSee whiteboard.
Cardinal Numbers 102/144
EquinumerosityRemarkA one-to-one function from 𝐴 onto 𝐵 is called a one-to-one correspond-ence between 𝐴 and 𝐵.
DefinitionA set 𝐴 is equinumerous to a set 𝐵, denoted 𝐴 ≈ 𝐵, iff there is a one-to-one correspondence between 𝐴 and 𝐵.
ExamplesSee whiteboard.
Cardinal Numbers 103/144
EquinumerosityRemarkA one-to-one function from 𝐴 onto 𝐵 is called a one-to-one correspond-ence between 𝐴 and 𝐵.
DefinitionA set 𝐴 is equinumerous to a set 𝐵, denoted 𝐴 ≈ 𝐵, iff there is a one-to-one correspondence between 𝐴 and 𝐵.
ExamplesSee whiteboard.
Cardinal Numbers 104/144
Equinumerosity
“The possibility that whole and partmay have the same number of termsis, it must be confessed, shocking tocommon-sense.” (Russell 1903,p. 358)
Cardinal Numbers 105/144
Equinumerosity
Theorem 6B(a) (Enderton 1977)The set 𝜔 is not equinumerous to the set ℝ of real numbers.
Proof.Let’s suppose 𝜔 ≈ ℝ, that is, there is an one-to-one correspondence𝑓 ∶ 𝜔 → ℝ such that∗
𝑓(0) = 236.001 … ,𝑓(1) = −7.777 … ,𝑓(2) = 3.1415 … ,
⋮
(continues in the next slide)∗“We assume that a decimal expansion does not contain only the digit 9 from some
place on, so each real number has a unique decimal expansion.” (Hrbacek and Jech1999, Theorem 6.1, p. 90)
Cardinal Numbers 106/144
Equinumerosity
Proof (cont).Let 𝑥 = 0.𝑑1𝑑2𝑑3 … ∈ ℝ, where
𝑑𝑛+1 = {4, if 𝑓(𝑛) ≠ 4;5, if 𝑓(𝑛) = 4.
The number 𝑥 doesn’t belong to the above enumeration. Therefore, ℝ isnon-enumerable.
Cardinal Numbers 107/144
On Refutations of Cantor’s Diagonal Argument“I dedicate this essay to thetwo-dozen-odd people whoserefutations of Cantor’s diagonalargument have come to me either asreferee or as editor in the last twentyyears or so... A few years ago itoccurred to me to wonder why somany people devote so much energyto refuting this harmless littleargument—what had it done tomake them angry with it?... Thesepages report the results.” (Hodges1998, p. 1)
Cardinal Numbers 108/144
Finite SetsDefinitionA set is finite iff it is equinumerous to some natural number. Otherwise itis infinite.
Corollary 6C (Enderton 1977)No finite set is equinumerous to a proper subset of itself.
Corollary 6D (Enderton 1977)1. Any set equinumerous to a proper subset of itself is infinite.2. The set 𝜔 is infinite.
Cardinal Numbers 109/144
Finite SetsDefinitionA set is finite iff it is equinumerous to some natural number. Otherwise itis infinite.
Corollary 6C (Enderton 1977)No finite set is equinumerous to a proper subset of itself.
Corollary 6D (Enderton 1977)1. Any set equinumerous to a proper subset of itself is infinite.2. The set 𝜔 is infinite.
Cardinal Numbers 110/144
Finite SetsDefinitionA set is finite iff it is equinumerous to some natural number. Otherwise itis infinite.
Corollary 6C (Enderton 1977)No finite set is equinumerous to a proper subset of itself.
Corollary 6D (Enderton 1977)1. Any set equinumerous to a proper subset of itself is infinite.
2. The set 𝜔 is infinite.
Cardinal Numbers 111/144
Finite SetsDefinitionA set is finite iff it is equinumerous to some natural number. Otherwise itis infinite.
Corollary 6C (Enderton 1977)No finite set is equinumerous to a proper subset of itself.
Corollary 6D (Enderton 1977)1. Any set equinumerous to a proper subset of itself is infinite.2. The set 𝜔 is infinite.
Cardinal Numbers 112/144
The Continuum Hypothesis
The continuum hypothesis (CH)There is no a set whose cardinality is strictly between the cardinality of theset of the natural numbers and the cardinality of the set of real numbers,that is,
2ℵ0 = ℵ1.CH could not be disproved (Gödel 1938) nor proved (Cohen 1963) in ZFC,that is, CH is independent of ZFC set theory.
Cardinal Numbers 113/144
Orderings and Ordinals
Well OrderingsDefinitionA well ordering on 𝐴 is a linear ordering on 𝐴 with the further propertythat every nonempty subset of 𝐴 has a least element.
DefinitionA structure is a pair ⟨𝐴, 𝑅⟩ consisting of a set 𝐴 and a binary relation 𝑅on 𝐴.
Orderings and Ordinals 115/144
Well OrderingsDefinitionA well ordering on 𝐴 is a linear ordering on 𝐴 with the further propertythat every nonempty subset of 𝐴 has a least element.
DefinitionA structure is a pair ⟨𝐴, 𝑅⟩ consisting of a set 𝐴 and a binary relation 𝑅on 𝐴.
Orderings and Ordinals 116/144
Transfinite Induction PrincipleDefinitionLet < be some sort of ordering on 𝐴 and 𝑡 ∈ 𝐴. The set
seg 𝑡 = {𝑥 ∣ 𝑥 < 𝑡}
is called the initial segment up to 𝑡.
Transfinite induction principleLet ⟨𝐴, <⟩ be a well-ordered structure and assume that 𝐵 is a subset of 𝐴with the special property that for every 𝑡 in 𝐴,
seg 𝑡 ⊆ 𝐵 ⇒ 𝑡 ∈ 𝐵.
Then 𝐵 coincides with 𝐴.
Orderings and Ordinals 117/144
Transfinite Induction PrincipleDefinitionLet < be some sort of ordering on 𝐴 and 𝑡 ∈ 𝐴. The set
seg 𝑡 = {𝑥 ∣ 𝑥 < 𝑡}
is called the initial segment up to 𝑡.
Transfinite induction principleLet ⟨𝐴, <⟩ be a well-ordered structure and assume that 𝐵 is a subset of 𝐴with the special property that for every 𝑡 in 𝐴,
seg 𝑡 ⊆ 𝐵 ⇒ 𝑡 ∈ 𝐵.
Then 𝐵 coincides with 𝐴.
Orderings and Ordinals 118/144
IsomorphismsDefinitionLet ⟨𝐴, 𝑅⟩ and ⟨𝐵, 𝑆⟩ be two structures. An isomorphism from ⟨𝐴, 𝑅⟩onto ⟨𝐵, 𝑆⟩ is a one-to-one function 𝑓 from 𝐴 onto 𝐵 such that for all𝑥, 𝑦 ∈ 𝐴
𝑥𝑅𝑦 iff 𝑓(𝑥)𝑆𝑓(𝑦).
Orderings and Ordinals 119/144
Ordinal NumbersDefinitionLet < be a well-ordering on 𝐴. The ordinal number of ⟨𝐴, <⟩ is its 𝜀-image. An ordinal number is a set that is the ordinal number of somewell-ordered structure.
Orderings and Ordinals 120/144
Ordinal NumbersDefinitionA set 𝐴 is well-ordered by epsilon iff the relation
∈𝐴= {⟨𝑥, 𝑦⟩ ∈ 𝐴 × 𝐴 ∣ 𝑥 ∈ 𝑦}
is a well ordering on 𝐴.
Remark (other definition of ordinal number)A set 𝐴 is an ordinal number iff (Hrbacek and Jech 1999, p. 107):
1. The set is transitive.2. The set is well-ordered by ∈𝐴.
Orderings and Ordinals 121/144
Ordinal NumbersDefinitionA set 𝐴 is well-ordered by epsilon iff the relation
∈𝐴= {⟨𝑥, 𝑦⟩ ∈ 𝐴 × 𝐴 ∣ 𝑥 ∈ 𝑦}
is a well ordering on 𝐴.
Remark (other definition of ordinal number)A set 𝐴 is an ordinal number iff (Hrbacek and Jech 1999, p. 107):
1. The set is transitive.2. The set is well-ordered by ∈𝐴.
Orderings and Ordinals 122/144
Ordinal NumbersRemarkThe class of ordinal numbers, denoted by On, can be defined by (Rubin1967, pp. 175-176):
0 ∈ On,𝛼 ∈ On ⇒ 𝛼+ ∈ On,
𝐴 ⊆ On ⇒ ⋃ 𝐴 ∈ On.
See also Enderton (1977, Corollary 7N).
Burali-Forti theoremThere is no set to which every ordinal number belongs.
Orderings and Ordinals 123/144
Ordinal NumbersRemarkThe class of ordinal numbers, denoted by On, can be defined by (Rubin1967, pp. 175-176):
0 ∈ On,𝛼 ∈ On ⇒ 𝛼+ ∈ On,
𝐴 ⊆ On ⇒ ⋃ 𝐴 ∈ On.
See also Enderton (1977, Corollary 7N).
Burali-Forti theoremThere is no set to which every ordinal number belongs.
Orderings and Ordinals 124/144
Ordinal NumbersRemarkThe class of ordinal numbers, denoted by On, can be defined by (Rubin1967, pp. 175-176):
0 ∈ On,𝛼 ∈ On ⇒ 𝛼+ ∈ On,
𝐴 ⊆ On ⇒ ⋃ 𝐴 ∈ On.
See also Enderton (1977, Corollary 7N).
Burali-Forti theoremThere is no set to which every ordinal number belongs.
Orderings and Ordinals 125/144
Cardinal NumbersDefinitionLet 𝐴 be a set. The cardinal number of 𝐴, denoted card 𝐴, is the leastordinal equinumerous to 𝐴.
DefinitionAn ordinal number is an initial ordinal iff it is not equinumerous to anysmaller ordinal number.
RemarkCardinal numbers and initial ordinals are the same numbers.
Orderings and Ordinals 126/144
Cardinal NumbersDefinitionLet 𝐴 be a set. The cardinal number of 𝐴, denoted card 𝐴, is the leastordinal equinumerous to 𝐴.
DefinitionAn ordinal number is an initial ordinal iff it is not equinumerous to anysmaller ordinal number.
RemarkCardinal numbers and initial ordinals are the same numbers.
Orderings and Ordinals 127/144
Cardinal NumbersDefinitionLet 𝐴 be a set. The cardinal number of 𝐴, denoted card 𝐴, is the leastordinal equinumerous to 𝐴.
DefinitionAn ordinal number is an initial ordinal iff it is not equinumerous to anysmaller ordinal number.
RemarkCardinal numbers and initial ordinals are the same numbers.
Orderings and Ordinals 128/144
Ordinals and Order Types
Ordinal AdditionDefinitionLet 𝛼 and 𝛽 be ordinals. We define their addition by transfinite recursionon 𝛽:
𝛼 + 0 = 𝛼,
𝛼 + 𝛽+ = (𝛼 + 𝛽)+,
𝛼 + 𝛽 = sup{𝛼 + 𝜆 ∣ 𝜆 < 𝛽}= ⋃
𝜆<𝛽(𝛼 + 𝜆), for all limit ordinal 𝛽.
Ordinals and Order Types 130/144
Ordinal AdditionExamples𝜔 = 1 + 𝜔, 1 + 𝜔 ≠ 𝜔 + 1 and 𝜔 + 1 ≠ 𝜔 + 𝜔.
0123
⋮
(a) 𝜔⟨0, 0⟩⟨0, 1⟩⟨1, 1⟩⟨2, 1⟩
⋮
(b) 1 + 𝜔⟨0, 0⟩⟨1, 0⟩⟨2, 0⟩
⋮⟨0, 1⟩
(c) 𝜔 + 1⟨0, 0⟩⟨1, 0⟩⟨2, 0⟩
⋮⟨0, 1⟩⟨1, 1⟩⟨2, 1⟩
⋮
(d) 𝜔 + 𝜔
Ordinals and Order Types 131/144
List of Axioms
Extensionality axiom: If two sets have exactly the same members,then they are equal, that is,
∀𝐴 ∀𝐵 [∀𝑥 (𝑥 ∈ 𝐴 ⇔ 𝑥 ∈ 𝐵) ⇒ 𝐴 = 𝐵].
Empty (existence) axiom: There is a set having no members,that is,
∃𝐵 ∀𝑥 (𝑥 ∉ 𝐵).
Pairing axiom: For any sets 𝑢 and 𝑣, there is a set having asmembers just 𝑢 and 𝑣, that is,
∀𝑎 ∀𝑏 ∃𝐶 ∀𝑥 (𝑥 ∈ 𝐶 ⇔ 𝑥 = 𝑎 ∨ 𝑥 = 𝑏).
List of Axioms 132/144
List of Axioms
Union axiom (first version): For any sets 𝑎 and 𝑏, there is a setwhose members are those sets belonging either to 𝑎 or to 𝑏 (or both),that is,
∀𝑎 ∀𝑏 ∃𝐵 ∀𝑥 (𝑥 ∈ 𝐵 ⇔ 𝑥 ∈ 𝑎 ∨ 𝑥 ∈ 𝑏).
Union axiom (final version): For any set 𝐴, there exists a set 𝐵whose elements are exactly the members of the members of 𝐴, that is,
∀𝐴 ∃𝐵 ∀𝑥 [𝑥 ∈ 𝐵 ⇔ ∃𝑏 (𝑏 ∈ 𝐴 ∧ 𝑥 ∈ 𝑏)].
Power set axiom: For any set 𝑎, there is a set whose members areexactly the subsets of 𝑎, that is,
∀𝑎 ∃𝐵 ∀𝑥 (𝑥 ∈ 𝐵 ⇔ 𝑥 ⊆ 𝑎).
List of Axioms 133/144
List of Axioms
Subset axiom scheme (axiom scheme of comprehension orseparation): For each formula 𝜑, not containing 𝐵, the following is anaxiom:
∀𝑡1 ⋯ ∀𝑡𝑘 ∀𝑐 ∃𝐵 ∀𝑥 (𝑥 ∈ 𝐵 ⇔ 𝑥 ∈ 𝑐 ∧ 𝜑).
Infinity axiom: There exists an inductive set, that is,
∃𝐴 [∅ ∈ 𝐴 ∧ ∀𝑎 (𝑎 ∈ 𝐴 ⇒ 𝑎+ ∈ 𝐴)].
Axiom of choice (a version): For any relation 𝑅 there is a function𝐹 ⊆ 𝑅 with dom 𝐹 = dom 𝑅.
List of Axioms 134/144
List of Axioms
Regularity (foundation) axiom: All sets are well-founded, that is,
∀𝐴 [𝐴 ≠ ∅ ⇒ ∃𝑚 (𝑚 ∈ 𝐴 ∧ 𝑚 ∩ 𝐴 = ∅)].
List of Axioms 135/144
Appendix: Formal Proofs
Proofs by Contradiction and Proofs of Negations
Proof by contradiction(or reductio ad absurdum)
[¬𝛽]⋮
⊥𝛽
Proof of negation (Bauer 2017)
[𝛽]⋮⊥¬𝛽
Justifications
[¬𝛽]⋮
⊥ (conditional proof)¬𝛽 → ⊥(¬𝛼 def= 𝛼 → ⊥)¬¬𝛽 (⊢ ¬¬𝛼 → 𝛼)𝛽
[𝛽]⋮
⊥ (conditional proof)𝛽 → ⊥(¬𝛼 def= 𝛼 → ⊥)¬𝛽
Appendix: Formal Proofs 137/144
Proofs by Contradiction and Proofs of Negations
Proof by contradiction(or reductio ad absurdum)
[¬𝛽]⋮
⊥𝛽
Proof of negation (Bauer 2017)
[𝛽]⋮⊥¬𝛽
Justifications
[¬𝛽]⋮
⊥ (conditional proof)¬𝛽 → ⊥(¬𝛼 def= 𝛼 → ⊥)¬¬𝛽 (⊢ ¬¬𝛼 → 𝛼)𝛽
[𝛽]⋮
⊥ (conditional proof)𝛽 → ⊥(¬𝛼 def= 𝛼 → ⊥)¬𝛽
Appendix: Formal Proofs 138/144
Natural Deduction: Derivations Rules𝜑 𝜓 ∧I𝜑 ∧ 𝜓
𝜑 ∧ 𝜓 ∧E𝜑𝜑 ∧ 𝜓 ∧E𝜓
[𝜑]⋮𝜓 →I𝜑 → 𝜓
𝜑 𝜑 → 𝜓 →E𝜓
𝜑 ∨I𝜑 ∨ 𝜓𝜓 ∨I𝜑 ∨ 𝜓 𝜑 ∨ 𝜓
[𝜑]⋮𝜎
[𝜓]⋮𝜎 ∨E𝜎
⊥ ⊥E𝜑
[¬𝜑]⋮⊥ RAA𝜑
Appendix: Formal Proofs 139/144
Natural Deduction: Derivations RulesRemarks
Abbreviations: ¬𝜑 ∶= 𝜑 → ⊥.In the application of the →I rule, we may discharge zero, one, or moreoccurrences of the assumption.References: See, for example, (van Dalen 2013).
Appendix: Formal Proofs 140/144
Examples
Example (van Dalen (2013), example p. 49)Proof of ⊢ 𝜑 ∨ ¬𝜑.
[𝜑]1∨I𝜑 ∨ ¬𝜑 [¬(𝜑 ∨ ¬𝜑)]2
→E⊥
→I1¬𝜑∨I𝜑 ∨ ¬𝜑 [¬(𝜑 ∨ ¬𝜑)]2
→E⊥
RAA2𝜑 ∨ ¬𝜑
Appendix: Formal Proofs 141/144
References
Aczel, Peter (1977). An Introduction to Inductive Definitions. In: Handbookof Mathematical Logic. Ed. by Barwise, Jon. Vol. 90. Studies in Logic andthe Foundations of Mathematics. Elsevier. Chap. C.7. doi:10.1016/S0049-237X(08)71120-0 (cit. on p. 100).Bauer, Andrej (2017). Five States of Accepting Constructive Mathematics.Bull. Amer. Math. Soc. 54.3, pp. 481–498. doi: 10.1090/bull/1556(cit. on pp. 137, 138).Benacerraf, Paul (1965). What Numbers Could not Be. The PhilosophicalReview 74.1, pp. 47–73. doi: 10.2307/2183530 (cit. on p. 99).Cantor, Georg (1895). Beiträge zur Begründung der transfinitenMengenlehre. Mathematische Annalen 46.4, pp. 481–512. doi:10.1007/BF02124929 (cit. on pp. 7, 8).Cohen, Paul J. (1963). The Independence of the Continuum Hypothesis.Proceedings of the National Academy of Sciences of the United States ofAmerica 50.6, pp. 1143–1148. url:http://www.pnas.org/content/50/6/1143.full.pdf (cit. on p. 113).
ReferencesEnderton, Herbert B. (1977). Elements of Set Theory. Academic Press(cit. on pp. 19, 20, 25, 41, 42, 45–48, 55, 56, 58, 59, 67–70, 92–98, 106,109–112, 123–125).Gödel, Kurt (1938). The Consistency of the Axiom of Choice and of theGeneralized Continuum-Hypothesis. Proceedings of the National Academyof Sciences of the United States of America 24.12, pp. 556–557. doi:10.1073/pnas.24.12.556 (cit. on p. 113).Hamilton, A. G. (1978). Logic for Mathematicians. Revised edition.Cambridge University Press (cit. on pp. 27, 28).Hodges, Wilfrid (1998). An Editor Recalls some Hopeless Papers. TheBulletin of Symbolic Logic 4.1, pp. 1–16. doi: 10.2307/421003 (cit. onp. 108).Hrbacek, Karel and Jech, Thomas (1999). Introduction to Set Theory.Third Edition, Revised and Expanded. Marcel Dekker (cit. on pp. 7, 8,11–13, 25, 86, 96, 97, 106, 121, 122).
ReferencesMartin-Löf, Per (2006). 100 Years of Zermelo’s Axiom of Choice: What wasthe Problem with It? The Computer Journal 49.3, pp. 345–350. doi:10.1093/comjnl/bxh162 (cit. on pp. 60, 61).Mendelson, Elliott (1997). Introduction to Mathematical Logic. 4th ed.Chapman & Hall (cit. on pp. 27, 28).Moore, Gregory H. (1982). Zermelo’s Axiom of Choice. Its Origins,Development, and Influence. Springer-Verlag (cit. on pp. 11–13).Rubin, Jean E. (1967). Set Theory for the Mathematician. Holden-Day(cit. on pp. 123–125).Russell, Bertrand (1903). The Principles of Mathematics. W. W. Norton &Company, Inc (cit. on p. 105).van Dalen, Dirk (2013). Logic and Structure. 5th ed. Springer (cit. onpp. 140, 141).van Heijenoort, Jean (1967). From Frege to Gödel: A Source Book inMathematical Logic, 1879-1931. Harvard University Press (cit. on p. 17).
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