SET THEORY I, FALL 2019 COURSE NOTES Contents 1. Introduction
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A Proof of Cantor's Theorem
Cantor’s Theorem Joe Roussos 1 Preliminary ideas Two sets have the same number of elements (are equinumerous, or have the same cardinality) iff there is a bijection between the two sets. Mappings: A mapping, or function, is a rule that associates elements of one set with elements of another set. We write this f : X ! Y , f is called the function/mapping, the set X is called the domain, and Y is called the codomain. We specify what the rule is by writing f(x) = y or f : x 7! y. e.g. X = f1; 2; 3g;Y = f2; 4; 6g, the map f(x) = 2x associates each element x 2 X with the element in Y that is double it. A bijection is a mapping that is injective and surjective.1 • Injective (one-to-one): A function is injective if it takes each element of the do- main onto at most one element of the codomain. It never maps more than one element in the domain onto the same element in the codomain. Formally, if f is a function between set X and set Y , then f is injective iff 8a; b 2 X; f(a) = f(b) ! a = b • Surjective (onto): A function is surjective if it maps something onto every element of the codomain. It can map more than one thing onto the same element in the codomain, but it needs to hit everything in the codomain. Formally, if f is a function between set X and set Y , then f is surjective iff 8y 2 Y; 9x 2 X; f(x) = y Figure 1: Injective map. -
Linear Transformation (Sections 1.8, 1.9) General View: Given an Input, the Transformation Produces an Output
Linear Transformation (Sections 1.8, 1.9) General view: Given an input, the transformation produces an output. In this sense, a function is also a transformation. 1 4 3 1 3 Example. Let A = and x = 1 . Describe matrix-vector multiplication Ax 2 0 5 1 1 1 in the language of transformation. 1 4 3 1 31 5 Ax b 2 0 5 11 8 1 Vector x is transformed into vector b by left matrix multiplication Definition and terminologies. Transformation (or function or mapping) T from Rn to Rm is a rule that assigns to each vector x in Rn a vector T(x) in Rm. • Notation: T: Rn → Rm • Rn is the domain of T • Rm is the codomain of T • T(x) is the image of vector x • The set of all images T(x) is the range of T • When T(x) = Ax, A is a m×n size matrix. Range of T = Span{ column vectors of A} (HW1.8.7) See class notes for other examples. Linear Transformation --- Existence and Uniqueness Questions (Section 1.9) Definition 1: T: Rn → Rm is onto if each b in Rm is the image of at least one x in Rn. • i.e. codomain Rm = range of T • When solve T(x) = b for x (or Ax=b, A is the standard matrix), there exists at least one solution (Existence question). Definition 2: T: Rn → Rm is one-to-one if each b in Rm is the image of at most one x in Rn. • i.e. When solve T(x) = b for x (or Ax=b, A is the standard matrix), there exists either a unique solution or none at all (Uniqueness question). -
LINEAR ALGEBRA METHODS in COMBINATORICS László Babai
LINEAR ALGEBRA METHODS IN COMBINATORICS L´aszl´oBabai and P´eterFrankl Version 2.1∗ March 2020 ||||| ∗ Slight update of Version 2, 1992. ||||||||||||||||||||||| 1 c L´aszl´oBabai and P´eterFrankl. 1988, 1992, 2020. Preface Due perhaps to a recognition of the wide applicability of their elementary concepts and techniques, both combinatorics and linear algebra have gained increased representation in college mathematics curricula in recent decades. The combinatorial nature of the determinant expansion (and the related difficulty in teaching it) may hint at the plausibility of some link between the two areas. A more profound connection, the use of determinants in combinatorial enumeration goes back at least to the work of Kirchhoff in the middle of the 19th century on counting spanning trees in an electrical network. It is much less known, however, that quite apart from the theory of determinants, the elements of the theory of linear spaces has found striking applications to the theory of families of finite sets. With a mere knowledge of the concept of linear independence, unexpected connections can be made between algebra and combinatorics, thus greatly enhancing the impact of each subject on the student's perception of beauty and sense of coherence in mathematics. If these adjectives seem inflated, the reader is kindly invited to open the first chapter of the book, read the first page to the point where the first result is stated (\No more than 32 clubs can be formed in Oddtown"), and try to prove it before reading on. (The effect would, of course, be magnified if the title of this volume did not give away where to look for clues.) What we have said so far may suggest that the best place to present this material is a mathematics enhancement program for motivated high school students. -
Sunflower Theorems and Convex Codes
Sunflower Theorems and Convex Codes Amzi Jeffs University of Washington, Seattle UC Davis Algebra & Discrete Mathematics Seminar April 6th 2020 Part I: Place Cells and Convex Codes Place Cells 1971: O'Keefe and Dostrovsky describe place cells in the hippocampus of rats. 2019 video: https://youtu.be/puCV1grkdJA Main idea: Each place cell fires in a particular region. They \know" where the rat is. How much do they know? Mathematical Model of Place Cells 2013: Curto et al introduce convex neural codes. Index your place cells (neurons) by [n] def= f1; 2; : : : ; ng. d Each neuron i 2 [n] fires when rat is in convex open Ui in R . As rat moves, multiple neurons may fire at same time. Write down all the sets of neurons that fire together and get a convex neural code C ⊆ 2[n]. Example with 3 neurons in R2: Formal Definitions Definition A code is any subset of 2[n]. Elements of a code are codewords. Definition d Let U = fU1;:::;Ung be a collection of convex open sets in R . The code of U is def d code(U) = σ ⊆ [n] There is p 2 R with p 2 Ui , i 2 σ : The collection U is called a convex realization. Codes that have convex realizations are called convex. Notice! If the Ui correspond to place cells, we can compute code(U) from the brain directly (if the rat explores sufficiently...) Terminology in Practice Below, U = fU1;U2;U3g is a convex realization of C = f123; 12; 23; 2; 3; ;g. Therefore C is a convex code. -
Functions and Inverses
Functions and Inverses CS 2800: Discrete Structures, Spring 2015 Sid Chaudhuri Recap: Relations and Functions ● A relation between sets A !the domain) and B !the codomain" is a set of ordered pairs (a, b) such that a ∈ A, b ∈ B !i.e. it is a subset o# A × B" Cartesian product – The relation maps each a to the corresponding b ● Neither all possible a%s, nor all possible b%s, need be covered – Can be one-one, one&'an(, man(&one, man(&man( Alice CS 2800 Bob A Carol CS 2110 B David CS 3110 Recap: Relations and Functions ● ) function is a relation that maps each element of A to a single element of B – Can be one-one or man(&one – )ll elements o# A must be covered, though not necessaril( all elements o# B – Subset o# B covered b( the #unction is its range/image Alice Balch Bob A Carol Jameson B David Mews Recap: Relations and Functions ● Instead of writing the #unction f as a set of pairs, e usually speci#y its domain and codomain as: f : A → B * and the mapping via a rule such as: f (Heads) = 0.5, f (Tails) = 0.5 or f : x ↦ x2 +he function f maps x to x2 Recap: Relations and Functions ● Instead of writing the #unction f as a set of pairs, e usually speci#y its domain and codomain as: f : A → B * and the mapping via a rule such as: f (Heads) = 0.5, f (Tails) = 0.5 2 or f : x ↦ x f(x) ● Note: the function is f, not f(x), – f(x) is the value assigned b( f the #unction f to input x x Recap: Injectivity ● ) function is injective (one-to-one) if every element in the domain has a unique i'age in the codomain – +hat is, f(x) = f(y) implies x = y Albany NY New York A MA Sacramento B CA Boston .. -
An Epsilon of Room, II: Pages from Year Three of a Mathematical Blog
An Epsilon of Room, II: pages from year three of a mathematical blog Terence Tao This is a preliminary version of the book An Epsilon of Room, II: pages from year three of a mathematical blog published by the American Mathematical Society (AMS). This preliminary version is made available with the permission of the AMS and may not be changed, edited, or reposted at any other website without explicit written permission from the author and the AMS. Author's preliminary version made available with permission of the publisher, the American Mathematical Society Author's preliminary version made available with permission of the publisher, the American Mathematical Society To Garth Gaudry, who set me on the road; To my family, for their constant support; And to the readers of my blog, for their feedback and contributions. Author's preliminary version made available with permission of the publisher, the American Mathematical Society Author's preliminary version made available with permission of the publisher, the American Mathematical Society Contents Preface ix A remark on notation x Acknowledgments xi Chapter 1. Expository articles 1 x1.1. An explicitly solvable nonlinear wave equation 2 x1.2. Infinite fields, finite fields, and the Ax-Grothendieck theorem 8 x1.3. Sailing into the wind, or faster than the wind 15 x1.4. The completeness and compactness theorems of first-order logic 24 x1.5. Talagrand's concentration inequality 43 x1.6. The Szemer´edi-Trotter theorem and the cell decomposition 50 x1.7. Benford's law, Zipf's law, and the Pareto distribution 58 x1.8. Selberg's limit theorem for the Riemann zeta function on the critical line 70 x1.9. -
A Theory of Stationary Trees and the Balanced Baumgartner-Hajnal-Todorcevic Theorem for Trees
A Theory of Stationary Trees and the Balanced Baumgartner-Hajnal-Todorcevic Theorem for Trees by Ari Meir Brodsky A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Mathematics University of Toronto c Copyright 2014 by Ari Meir Brodsky Abstract A Theory of Stationary Trees and the Balanced Baumgartner-Hajnal-Todorcevic Theorem for Trees Ari Meir Brodsky Doctor of Philosophy Graduate Department of Mathematics University of Toronto 2014 Building on early work by Stevo Todorcevic, we develop a theory of stationary subtrees of trees of successor-cardinal height. We define the diagonal union of subsets of a tree, as well as normal ideals on a tree, and we characterize arbitrary subsets of a non-special tree as being either stationary or non-stationary. We then use this theory to prove the following partition relation for trees: Main Theorem. Let κ be any infinite regular cardinal, let ξ be any ordinal such that 2jξj < κ, and let k be any natural number. Then <κ 2 non- 2 -special tree ! (κ + ξ)k : This is a generalization to trees of the Balanced Baumgartner-Hajnal-Todorcevic Theorem, which we recover by applying the above to the cardinal (2<κ)+, the simplest example of a non-(2<κ)-special tree. As a corollary, we obtain a general result for partially ordered sets: Theorem. Let κ be any infinite regular cardinal, let ξ be any ordinal such that 2jξj < κ, and let k be <κ 1 any natural number. Let P be a partially ordered set such that P ! (2 )2<κ . -
The Sunflower Lemma of Erdős and Rado
The Sunflower Lemma of Erdős and Rado René Thiemann March 1, 2021 Abstract We formally define sunflowers and provide a formalization ofthe sunflower lemma of Erdős and Rado: whenever a set ofsize-k-sets has a larger cardinality than (r − 1)k · k!, then it contains a sunflower of cardinality r. 1 Sunflowers Sunflowers are sets of sets, such that whenever an element is contained in at least two of the sets, then it is contained in all of the sets. theory Sunflower imports Main HOL−Library:FuncSet begin definition sunflower :: 0a set set ) bool where sunflower S = (8 x: (9 AB: A 2 S ^ B 2 S ^ A 6= B ^ x 2 A ^ x 2 B) −! (8 A: A 2 S −! x 2 A)) lemma sunflower-subset: F ⊆ G =) sunflower G =) sunflower F hproof i lemma pairwise-disjnt-imp-sunflower: pairwise disjnt F =) sunflower F hproof i lemma card2-sunflower: assumes finite S and card S ≤ 2 shows sunflower S hproof i lemma empty-sunflower: sunflower fg hproof i lemma singleton-sunflower: sunflower fAg hproof i 1 lemma doubleton-sunflower: sunflower fA;Bg hproof i lemma sunflower-imp-union-intersect-unique: assumes sunflower S and x 2 (S S) − (T S) shows 9 ! A: A 2 S ^ x 2 A hproof i lemma union-intersect-unique-imp-sunflower: V S T assumes x: x 2 ( S) − ( S) =) 9 ≤1 A: A 2 S ^ x 2 A shows sunflower S hproof i lemma sunflower-iff-union-intersect-unique: sunflower S ! (8 x 2 S S − T S: 9 ! A: A 2 S ^ x 2 A) (is ?l = ?r) hproof i lemma sunflower-iff-intersect-Uniq: T sunflower S ! (8 x: x 2 S _ (9 ≤1 A: A 2 S ^ x 2 A)) (is ?l = ?r) hproof i If there exists sunflowers whenever all elements are sets of thesame cardinality r, then there also exists sunflowers whenever all elements are sets with cardinality at most r. -
Lecture Notes: Axiomatic Set Theory
Lecture Notes: Axiomatic Set Theory Asaf Karagila Last Update: May 14, 2018 Contents 1 Introduction 3 1.1 Why do we need axioms?...............................3 1.2 Classes and sets.....................................4 1.3 The axioms of set theory................................5 2 Ordinals, recursion and induction7 2.1 Ordinals.........................................8 2.2 Transfinite induction and recursion..........................9 2.3 Transitive classes.................................... 10 3 The relative consistency of the Axiom of Foundation 12 4 Cardinals and their arithmetic 15 4.1 The definition of cardinals............................... 15 4.2 The Aleph numbers.................................. 17 4.3 Finiteness........................................ 18 5 Absoluteness and reflection 21 5.1 Absoluteness...................................... 21 5.2 Reflection........................................ 23 6 The Axiom of Choice 25 6.1 The Axiom of Choice.................................. 25 6.2 Weak version of the Axiom of Choice......................... 27 7 Sets of Ordinals 31 7.1 Cofinality........................................ 31 7.2 Some cardinal arithmetic............................... 32 7.3 Clubs and stationary sets............................... 33 7.4 The Club filter..................................... 35 8 Inner models of ZF 37 8.1 Inner models...................................... 37 8.2 Gödel’s constructible universe............................. 39 1 8.3 The properties of L ................................... 41 8.4 Ordinal definable sets................................. 42 9 Some combinatorics on ω1 43 9.1 Aronszajn trees..................................... 43 9.2 Diamond and Suslin trees............................... 44 10 Coda: Games and determinacy 46 2 Chapter 1 Introduction 1.1 Why do we need axioms? In modern mathematics, axioms are given to define an object. The axioms of a group define the notion of a group, the axioms of a Banach space define what it means for something to be a Banach space. -
Coding for Sunflowers
Coding for Sunflowers Anup Rao University of Washington [email protected] January 8, 2020 Abstract A sunflower is a family of sets that have the same pairwise intersections. We simplify a recent result of Alweiss, Lovett, Wu and Zhang that gives an upper bound on the size of every family of sets of size k that does not contain a sunflower. We show how to use the converse of Shannon's noiseless coding theorem to give a cleaner proof of a similar bound. 1 Introduction A p-sunflower is a family of p sets whose pairwise intersections are identical. How large can a family of sets of size k be if the family does not contain a p-sunflower? Erd}osand Rado [ER60] were the first to pose and answer this question. They showed that any family with more than (p−1)k ·k! sets of size k must contain a p-sunflower. This fundamental fact has many applications in mathematics and computer science [ES92, Raz85, FMS97, GM07, GMR13, Ros14, RR18, LZ19, LSZ19]. After nearly 60 years, the correct answer to this question is still not known. There is a family of (p−1)k sets of size k that does not contain a p-sunflower, and Erd}osand Rado conjectured that their lemma could be improved to show that this is essentially the extremal example. Recently, Alweiss, Lovett, Wu and Zhang [ALWZ19] made substantial progress towards resolving the conjecture. They showed that (log k)k · (p log log k)O(k) sets ensure the presence of a p-sunflower. -
On Sunflowers and Matrix Multiplication
On Sunflowers and Matrix Multiplication Noga Alon ∗ Amir Shpilka y Christopher Umans z Abstract We present several variants of the sunflower conjecture of Erdos˝ and Rado [ER60] and discuss the relations among them. We then show that two of these conjectures (if true) imply negative answers to questions of Cop- persmith and Winograd [CW90] and Cohn et al [CKSU05] regarding possible approaches for obtaining fast matrix multiplication algorithms. Specifically, we show that the Erdos-Rado˝ sunflower conjecture (if true) implies a negative answer to the “no three disjoint equivoluminous subsets” question of Copper- Zn smith and Winograd [CW90]; we also formulate a “multicolored” sunflower conjecture in 3 and show that (if true) it implies a negative answer to the “strong USP” conjecture of [CKSU05] (although it does not seem to impact a second conjecture in [CKSU05] or the viability of the general group-theoretic ap- proach). A surprising consequence of our results is that the Coppersmith-Winograd conjecture actually implies the Cohn et al. conjecture. Zn The multicolored sunflower conjecture in 3 is a strengthening of the well-known (ordinary) sun- Zn flower conjecture in 3 , and we show via our connection that a construction from [CKSU05] yields a lower bound of (2:51 :::)n on the size of the largest multicolored 3-sunflower-free set, which beats the current best known lower bound of (2:21 :::)n [Edel04] on the size of the largest 3-sunflower-free set in Zn 3 . ∗Sackler School of Mathematics and Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel and Institute for Advanced Study, Princeton, New Jersey, 08540, USA. -
Math 101 B-Packet
Math 101 B-Packet Scott Rome Winter 2012-13 1 Redefining functions This quarter we have defined a function as a rule which assigns exactly one output to each input, and so far we have been happy with this definition. Unfortunately, this way of thinking of a function is insufficient as things become more complicated in mathematics. For a better understanding of a function, we will first need to define it better. Definition 1.1. Let X; Y be any sets. A function f : X ! Y is a rule which assigns every element of X to an element of Y . The sets X and Y are called the domain and codomain of f respectively. x f(x) y Figure 1: This function f : X ! Y maps x 7! f(x). The green circle indicates the range of the function. Notice y is in the codomain, but f does not map to it. Remark 1.2. It is necessary to define the rule, the domain, and the codomain to define a function. Thus far in the class, we have been \sloppy" when working with functions. Remark 1.3. Notice how in the definition, the function is defined by three things: the rule, the domain, and the codomain. That means you can define functions that seem to be the same, but are actually different as we will see. The domain of a function can be thought of as the set of all inputs (that is, everything in the domain will be mapped somewhere by the function). On the other hand, the codomain of a function is the set of all possible outputs, and a function may not necessarily map to every element of the codomain.