An Application of First-Order Logic to a Problem in Combinatorics 1
|
|
- Jack Summers
- 6 years ago
- Views:
Transcription
1 An Application of First-Order Logic to a Problem in Combinatorics 1 I. The Combinatorial Background. A. Families of objects. In combinatorics, one frequently encounters a set of objects in which a), each object has an order n n = 0, 1,,...) and b), for each n, there is a finite number fn) of objects of that order. Here are three examples; the setting of this handout will be the third one. Example 1. Let S n be the set of permutations of the set [n] := {1,..., n} that is, the set of bijections from [n] onto [n]). It is easy to see that fn) = n! for n 1, and an argument can be made that one should put f0) := 1. Example. For n 1, let Π n be the set of all partitions of the set [n]; each of these can be identified with the corresponding equivalence relation on [n]. By direct listing, we get [{1,, 3}] Π 3 = [{1, }{3}] [{1, 3}{}] [{, 3}{1}] [{1}{}{3}] so that f3) = 5. In this case, there is a very strong argument for putting f0) := 1. Example 3. Informally, a labeled simple graph with vertex set [n] is drawing consisting of n dots, labeled 1,,... n, and of line segments or curves called edges) joining some of the pairs of dots. For n 1, let G n be the set of labeled simple graphs with vertex set [n]; a moment s thought will show that usually one puts f0) := 0. Exercise 1. Prove 1). Exercise. Draw all eight graphs in G 3. fn) = n ) = nn 1) n 1); 1) B. Properties in families. Sometimes, you want to count how many objects of order n have some special property. I will posit a possible property for each of the three examples. In S n, you might want to count the fixed-point-free permutations; that is, the permutations σ S n that satisfy A 1 : i)σi) i). ) In Π n, you might want to count the singleton-free partitions; that is, the partitions π Π n that satisfy A : i) j) i j) i j) ). 3) π In G n, you might want to count the isolated-point-free graphs; that is, the graphs g G n that satisfy A 3 : i) j)edgei, j)), 4) where edgei, j) means that in g there is an edge connecting vertices i and j. Observe that each of the properties above has been expressed by a suitably interpreted first-order wf over N; such a property is dubbed, naturally enough, a first-order property. Not every property is first-order; for example consider, for σ S n, the possible property of being reducible 3 that you can partition [n] into or more classes such that σ separately permutes the integers in each class. I doubt that the property of 1 This handout is a greatly expanded version of notes I took at a talk given by Peter Winkler at Swarthmore College in the early 90 s. This is the famous hatcheck problem. 3 This is not a standard term; the standard description would be that σ is not an n cycle. 1
2 reducibility can be expressed by a first-order wf ; it certainly doesn t smell first-order, defined as it is in terms of subsets of [n]. This handout will deal only with first-order properties. C. The probability of encountering a property. Let A be a first-order property in a family of objects that is, a closed first-order wf that, when interpreted, specifies a property in the family. Put limproba) := lim number of objects of order n with property A, 5) fn) if this limit exists; limproba) has pretty clear right to be named the probability that a randomly chosen object has property A. Consider the wf s in equations ), 3) and 4). It is well known that limproba 1 ) = 1 ; I do not know the e value of limproba ), or whether this limit even exists; 4 and as for limproba 3 ), you will be able to find this for yourself using the mathematics presented in this handout see Exercise 17). II. A Quick Review of Some Probability Theory. In an experiment in which different outcomes are possible, the sample space Ω is the set of all possible outcomes to the experiment; we will restrict ourselves to the case in which Ω is finite or denumerable. To each outcome in ω Ω, we assign a probability pω) 0 in such a way that 5 pω) = 1. An event is a subset of Ω. Each event A also is assigned a probability P A): Two events A and B are independent if ω Ω pω) if A ; P A) := ω A P ) = 0. P A B) = P A)P B); events A and B will be independent whenever the occurrence of one of them has no influence over whether the other one occurs. Exercise 3. Prove that the empty event is independent of every event A. A random variable is a numerical function defined on a sample space; for each number x, we use the notation X = x to denote the possibly empty) event {ω Ω: Xω) = x}. Random variables X and Y defined on the same sample space) are independent if the events X = x and Y = y are independent events for all x and y. The distribution of a random variable X is the possibly infinite) table x 1 p 1 = P X = x 1 ) x p = P X = x ) x 3 p 3 = P X = x 3 ) x 4 p 3 = P X = x 4 ).. where {x 1, x, x 3, x 4,...} are the values that X can assume). 4 But I may have an approach for finding this out. 5 It is impossible to proceed in this fashion if Ω is not denumerable.
3 The expected value of random variable X is the number E[X] := k 1 x k p k ; 6) it is easy to confirm the alternative formula E[X] = ω Ω Xω)pω), 7) in which the sum runs over the elements of the sample space. Exercise 4. Prove 7) from 6). If X and Y are two random variables on the same sample space then one defines their sum X + Y by X + Y )ω) := Xω) + Y ω); clearly X + Y is a third random variable on this sample space. Exercise 5. Using equation 7) and the definition of X + Y, show that E[X + Y ] = E[X] + E[Y ]. Note that this is true regardless whether or not X and Y are independent!) III. Random Graphs and the Statement of Fagin s Theorem. ) n You completely specify a graph g G n by stating whether each of the possible edges is present or ) n absent; if you do this by flipping a fair coin times and filling in an edge exactly when the corresponding flip comes up heads, you are conducting a probabilistic experiment, in which Ω is the set of all ) n possible sequences of coin flips, with pω) = 1 for each sequence ω Ω. Any first-order property A in the family ) n of graphs corresponds to an event A n in this sample space: A n := {ω Ω: the graph g ω constructed by ω has property A}. Moreover, since all sequences are equally likely, all graphs in G n have the same chance of being constructed. This implies that P A n ) = the number of graphs in G n with property A, ) n which together with 5) implies that limproba) = lim P A n). In this way, we can use the power of probability theory to help evaluate limproba). This insight is due to Paul Erdős and Alfréd Rényi, who, with the help of probability theory, calculated limproba) for a number of first-order properties A in the 1950 s, I think.) 6 The answers they got all turned out to be either 0 or 1, and they conjectured that this would always be the case. In 1976, Ronald Fagin proved this conjecture: Theorem Fagin, 1976). For every first-order property A of graphs, either limproba) = 1 or limproba) = 0. The remainder of this handout is an account of how Fagin proved this result. 6 I don t think that they singled out the class of first-order properties or even used the term. 3
4 IV. The First-Order System. The wf s that define first-order properties in this family will all be drawn from the following first-order system call it GT ). The first-order language will have no individual constants or function letters; the formal variables will be {x 1, x,... ; y 1, y,... ; z}. Since GT will turn out to be a first-order system with equality, the language will include the predicate letter A 1 nickname = ); in addition, there will be a predicate letter A nickname edge ). The axioms will include the Predicate Calculus axioms K1 K6 and the three equality axioms; in addition, there will be a few graph theory axioms, such as I suppose) and x 1 ) y 1 )edgex 1, y 1 ) x 1 = y 1 )) x 1 ) y 1 )edgex 1, y 1 ) edgey 1, x 1 )). Clearly, any graph g G n can be viewed as a model of GT, which I will call I g. I g is defined in the natural way: D Ig will be the vertex set [n]; A 1k, l) will be true if and only if k = l; and edgek, l) will be true if and only if there is an edge in g from vertex k to vertex l. V. The Alice s Restaurant Predicates. The good idea that opened up an approach to the proof was to single out the family of Alice s Restaurant of wf s: for each i 1 and j 1, let B i,j be the closed wf B i,j : x1 = y x 1 ) x i ) y 1 ) y j ) z) 1 ) x i = y j ) ) ) edgez, x1 ) edgez, x i ) edgez, y 1 ) edgez, y j ) ). }{{} ) The name for these wf s comes of course from the Arlo Guthrie song; 7 it was chosen because of the observation that a graph g with property B i,j that is, such that I g = B i,j ) gives you almost anything you want, to wit: S = i for any choice of sets S [n] and T [n], where T = j, g can supply a vertex z that is adjacent8 S T = to all the vertices in S and nonadjacent to any of the vertices in T. As you will see, the proof turns on the properties of the wf s {B i,j }. VI. The First Step of the Proof. Lemma 1. For each i 1 and j 1, limprobb i,j ) = 1. Proof. First: Fix i, j 1 and n i + j + 1). Choose from [n]: an i-element set S = {x 1,..., x i }; a j-element set T = {y 1,..., y j } disjoint from S; and an element z [n] S T ). Call the choice above CS, T, z). Let us say that a graph g G n succeeds with respect to CS, T, z) if, in g, there are edges from z to all of the x s and there are no edges from z to any of the y s; that is, if part ) of {B i,j } to the right of the quantifiers) interprets to a true statement in I g : edgez, x1 ) edgez, x i ) edgez, y 1 ) edgez, y j ) ). 7 My guess is that this is Winkler s own coinage; I can t imagine that Fagin was so flip in a research article. 8 Two vertices are adjacent if there is an edge between them. 4
5 Exercise 6. Express the condition g succeeds with respect to CS, T, z) as a condition on the valuations in I g. Exercise 7. Prove that the number of graphs g G n that succeed with respect to CS, T, z) is exactly n ) i+j). It follows from Exercise 7 that in Ω, so that ) i+j) P {ω: g ω succeeds with respect to CS, T, z)}) = n = ) n P {ω: g ω fails with respect to CS, T, z)}) = 1 It will help to have a name for this last quantity. Put so that 7) can be written α := 1 ) i+j 1, ) i+j 1 ; ) i+j 1. 7) P {ω: g ω fails with respect to CS, T, z)}) = α. 7 ) Second: passing to the negation. The reason for writing down 7 ) is this: the next task is to track what happens to the probabilities when we put the quantifiers back in front of ) to rebuild B i,j ; what the proof does is to track probabilities while putting quantifiers in front of ) to build B i,j. To make the rest of the argument easier to follow, I will construct this wf : B i,j : x1 = y x 1 ) x i ) y 1 ) y j ) z) 1 ) x i = y j ) ) ) edgez, x1 ) edgez, x i ) edgez, y 1 ) edgez, y j ) ). }{{} ) Third: incorporating the z). Consider what happens when you fix S and T but vary the choice of z. This gives rise to n i + j) distinct events {ω Ω: g ω fails with respect to CS, T, z t )} 1 t n i + j)). Each of these events clearly has probability α of occurring; and since they depend upon nonoverlapping sets of edges, they are all independent of each other. This implies that the probability that all of these events occur is α n i+j). In short, for fixed S and T : P {ω: g ω fails with respect to CS, T, z) for EVERY z / S T }) = α n i+j). 8) It will help to have a name for this event. Put FAILS, T ) := {ω: g ω fails with respect to CS, T, z) for EVERY z / S T }, so that 8) can be written P FAILS, T )) = α n i+j). 8 ) Fourth: incorporating the existential quantifiers. In order to so this, we will analyze the behavior of some random variables on Ω associated with the events {FAILS, T )}. For each possible choice of S and T, put 1, if ω FAILS, T ); Y S,T ω) := 0, else. 5
6 Exercise 8. Write down the distribution of Y S,T, and show that E[Y S,T ] = α n i+j). ) ) n n i Exercise 9. Show that there are exactly of the Y S,T s. i j We need one more random variable: the sum of all of the Y S,T s. Put X i,j = S [n] S =i T [n] T =j T S= Y S,T. 9) Exercise 10. Show, for any ω Ω, that X i,j ω) counts how many of the events {FAILS, T )} contain ω. It follows from Exercises 5 and 8 that ) n n i E[X i,j ] = i j ) α n i+j) = 1 α i+j n i ) n i Let us examine 10) more closely. Observe that for fixed positive integer k, the function gn) = ) n nn 1)n )...n k + 1) = k k! j ) α n. 10) ) ) n n i is a polynomial of degree k, so that hn) = is a polynomial in n of degree i + j). i j Since 0 < α < 1, it follows that ) ) lim 1 n n i i,j] = lim α i+j α n = 0. i j 11) But it is also easy to show that so that by the Squeeze Theorem, Exercise 11. Prove 11). Exercise 1. Prove 1). Exercise 13. Prove 13). Fifth and finally): A moment s thought shows that for any ω Ω, 0 P X i,j 1) E[X i,j ], 1) lim P X i,j 1) = 0. 13) X i,j ω) 1 I gω = B i,j, and this means that Equation 13) is equivalent to the statement which in turn is obviously equivalent to the statement Exercise 14. Explain why 14) is true. VII. The Second Step of the Proof. limprob B i,j ) = 0, 13 ) limprobb i,j ) = 1. Lemma 1) 14) We now extend GT by adjoining finite subsets of the set {B i,j }; the main point of this section will be to prove that all such extensions are consistent. 6
7 Lemma. Let T = {B 1),..., B k) } be a k element subset of {B i,j }. Then ThmGT T ) is a consistent first-order system. Proof. I will show this system to be consistent by demonstrating that it has a model. For 1 t k, let and put The point here is that if p T ) n p T ) n so that by Exercise 15, = 1 P MODELt) := {ω Ω: I gω = B t) }, MODELT ) := MODEL1) MODELk) and p T n ) := P MODELT )). > 0 for any n, then this system has an n vertex model.) First of all, clearly, ) ) MODELT ) = 1 P MODEL1) MODELk), 1 p T ) n 1 P Next, consider the limit of the right-hand-side of 15): lim 1 P MODELt)) ) = 1 = 1 by Equation 13 ) ) = 1 = 1. ) MODELt). 15) lim )) P MODELt) limprob B t) ) Because this limit equals 1, the Squeeze Theorem can be applied to the inequalities in 15), and doing so gives lim pt n ) = 1. 16) This certainly implies that p n > 0 for all large n, and for any such n, as mentioned above, the system has an n-vertex model. Lemma ) 0 Exercise 15. Prove, for any events {E 1,..., E n } of any sample space, that P E 1 E n ) P E 1 ) + + P E n ). VIII. The Third Step of the Proof. The next move is to adjoin the entire set {B i,j } to GT : let S be the first-order system ThmGT {B i,j }). By Lemma and the Compactness Theorem, S is consistent, so by the Löwenheim-Skolem Theorem, it has a denumerable model 9 which will be a simple labeled graph on a denumerable vertex set). The last big piece of the puzzle is Lemma 3, which will imply that S has only one denumerable model. 9 It occurs to me that our proof of the Löwenheim-Skolem Theorem does not cover GT, because GT has no closed terms. I am sure that the result is true for GT, though. 7
8 Lemma 3. Any two denumerable models of S are isomorphic. Proof. Observe first that any model of S will possess property B i,j for all i 1 and all j 1. Let G and H be two denumerable models of S ; I will construct an isomorphism between them. The first step is to enumerate the vertices of each graph: G = {g 1, g, g 3,...} H = {h 1, h, h 3,...}. The construction will iteratively reorder the vertices G = {ĝ 1, ĝ, ĝ 3,...} this will be done in such a way that for all i < j, H = {ĥ1, ĥ, ĥ3,...}; edgeĝ i, ĝ j ) edgeĥi, ĥj) 17) so that the function fĝ i ) = ĥi will be an isomorphism). { ĝ1 = g 1 The reordering starts by putting and The second step puts ĝ = g but lets ĥ be the first vertex ĥ 1 = h 1. that is adjacent/nonadjacent to ĥ1 according as ĝ is adjacent/nonadjacent to ĝ 1. Then third step makes ĥ3 the first unprocessed H vertex and lets ĝ 3 be the first vertex that will have adjacencies/nonadjacencies to ĝ 1 and ĝ that match those of ĥ3 to ĥ1 and ĥ. The construction proceeds in this fashion, alternating the rôles of G and H. Because G and H possess property B i,j for all i 1 and all j 1, the searches for vertices with the correct adjacency patterns will always succeed, so the process will not halt. Furthermore, the alternation of the graphs rôles guarantees that after at most n iterations, all of {g 1,..., g n } and {h 1,..., h n } will have been processed, so every vertex of each graph eventually be reached. Thirdly, after n iterations of the process, clearly the subgraphs induced by {ĝ 1, ĝ, ĝ 3,... ĝ n } 18) {ĥ1, ĥ, ĥ3,... ĥn} are isomorphic. Finally, to see that 17) is true for any i < j, just let n = j in 18): all four of {ĝ i, ĥi, ĝ j, ĥj} will be present. Lemma 3) Exercise 16. Show that S does not have any finite models. IX. Putting the Pieces Together. As mentioned earlier, Lemma 3 implies that S has only one denumerable model. An immediate consequence is Lemma 4. S is a consistent complete first-order system. Proof. We already have that S is consistent. Suppose S were not complete. Then there would be a closed wf A such that neither A nor A could be deduced in S. This would make both ThmS {A}) and ThmS { A}) consistent first-order systems. Then each of these systems would have a model. Each of these models would also be a model of S, but A would be true in one of them and false in the other. This is not possible, since, by Lemma 3, the two models must be isomorphic graphs. Lemma 4) Finally, let A be any closed wf. Since S is consistent and complete, exactly one of the statements A S is true, and limproba) is determined by which one it is. A S 8
9 Lemma 5. If A, then limproba) = 1; if S A, then limproba) = 0. S Proof. Clearly the two assertions are equivalent, so it suffices to prove the first one. Suppose that a particular deduction of it. Since the deduction is finite in length, it uses only a finite subset S A; fix T = {B 1),..., B k) } of the {B i,j }. This implies that A. 19) GT T Now consider any graph g G n. If g has all of the properties {B 1),..., B k) }, then by 19), g is a model for GT T ; in conjunction with 19), this implies that I g = A that is, g has property A). As an immediate consequence, we get 1 number of graphs in G n with property A n ) MODELT ) n ) Since lim pt n ) = 1 equation 16)), we can apply the Squeeze Theorem in 0) to get = p T ) n. 0) number of graphs in G n with property A limproba) = lim = 1. Lemma 5 and theorem) ) n Exercise 17. Find limproba 3 ) introduced at the end of Section I). 9
FIRST ORDER SENTENCES ON G(n, p), ZERO-ONE LAWS, ALMOST SURE AND COMPLETE THEORIES ON SPARSE RANDOM GRAPHS
FIRST ORDER SENTENCES ON G(n, p), ZERO-ONE LAWS, ALMOST SURE AND COMPLETE THEORIES ON SPARSE RANDOM GRAPHS MOUMANTI PODDER 1. First order theory on G(n, p) We start with a very simple property of G(n,
More informationInformal Statement Calculus
FOUNDATIONS OF MATHEMATICS Branches of Logic 1. Theory of Computations (i.e. Recursion Theory). 2. Proof Theory. 3. Model Theory. 4. Set Theory. Informal Statement Calculus STATEMENTS AND CONNECTIVES Example
More informationSample Spaces, Random Variables
Sample Spaces, Random Variables Moulinath Banerjee University of Michigan August 3, 22 Probabilities In talking about probabilities, the fundamental object is Ω, the sample space. (elements) in Ω are denoted
More informationIntroduction to Probability Theory, Algebra, and Set Theory
Summer School on Mathematical Philosophy for Female Students Introduction to Probability Theory, Algebra, and Set Theory Catrin Campbell-Moore and Sebastian Lutz July 28, 2014 Question 1. Draw Venn diagrams
More informationThe expansion of random regular graphs
The expansion of random regular graphs David Ellis Introduction Our aim is now to show that for any d 3, almost all d-regular graphs on {1, 2,..., n} have edge-expansion ratio at least c d d (if nd is
More informationIntroduction to Metalogic
Philosophy 135 Spring 2008 Tony Martin Introduction to Metalogic 1 The semantics of sentential logic. The language L of sentential logic. Symbols of L: Remarks: (i) sentence letters p 0, p 1, p 2,... (ii)
More informationDiscrete Mathematics and Probability Theory Fall 2014 Anant Sahai Note 15. Random Variables: Distributions, Independence, and Expectations
EECS 70 Discrete Mathematics and Probability Theory Fall 204 Anant Sahai Note 5 Random Variables: Distributions, Independence, and Expectations In the last note, we saw how useful it is to have a way of
More informationLecture 7: February 6
CS271 Randomness & Computation Spring 2018 Instructor: Alistair Sinclair Lecture 7: February 6 Disclaimer: These notes have not been subjected to the usual scrutiny accorded to formal publications. They
More informationCS 246 Review of Proof Techniques and Probability 01/14/19
Note: This document has been adapted from a similar review session for CS224W (Autumn 2018). It was originally compiled by Jessica Su, with minor edits by Jayadev Bhaskaran. 1 Proof techniques Here we
More informationDiscrete Mathematics and Probability Theory Fall 2013 Vazirani Note 12. Random Variables: Distribution and Expectation
CS 70 Discrete Mathematics and Probability Theory Fall 203 Vazirani Note 2 Random Variables: Distribution and Expectation We will now return once again to the question of how many heads in a typical sequence
More informationMath 564 Homework 1. Solutions.
Math 564 Homework 1. Solutions. Problem 1. Prove Proposition 0.2.2. A guide to this problem: start with the open set S = (a, b), for example. First assume that a >, and show that the number a has the properties
More informationA = A U. U [n] P(A U ). n 1. 2 k(n k). k. k=1
Lecture I jacques@ucsd.edu Notation: Throughout, P denotes probability and E denotes expectation. Denote (X) (r) = X(X 1)... (X r + 1) and let G n,p denote the Erdős-Rényi model of random graphs. 10 Random
More information20.1 2SAT. CS125 Lecture 20 Fall 2016
CS125 Lecture 20 Fall 2016 20.1 2SAT We show yet another possible way to solve the 2SAT problem. Recall that the input to 2SAT is a logical expression that is the conunction (AND) of a set of clauses,
More informationVC-DENSITY FOR TREES
VC-DENSITY FOR TREES ANTON BOBKOV Abstract. We show that for the theory of infinite trees we have vc(n) = n for all n. VC density was introduced in [1] by Aschenbrenner, Dolich, Haskell, MacPherson, and
More informationFoundations of Mathematics MATH 220 FALL 2017 Lecture Notes
Foundations of Mathematics MATH 220 FALL 2017 Lecture Notes These notes form a brief summary of what has been covered during the lectures. All the definitions must be memorized and understood. Statements
More informationCS280, Spring 2004: Final
CS280, Spring 2004: Final 1. [4 points] Which of the following relations on {0, 1, 2, 3} is an equivalence relation. (If it is, explain why. If it isn t, explain why not.) Just saying Yes or No with no
More information17.1 Correctness of First-Order Tableaux
Applied Logic Lecture 17: Correctness and Completeness of First-Order Tableaux CS 4860 Spring 2009 Tuesday, March 24, 2009 Now that we have introduced a proof calculus for first-order logic we have to
More informationLecture 11: Non-Interactive Zero-Knowledge II. 1 Non-Interactive Zero-Knowledge in the Hidden-Bits Model for the Graph Hamiltonian problem
CS 276 Cryptography Oct 8, 2014 Lecture 11: Non-Interactive Zero-Knowledge II Instructor: Sanjam Garg Scribe: Rafael Dutra 1 Non-Interactive Zero-Knowledge in the Hidden-Bits Model for the Graph Hamiltonian
More informationFundamentals of Probability CE 311S
Fundamentals of Probability CE 311S OUTLINE Review Elementary set theory Probability fundamentals: outcomes, sample spaces, events Outline ELEMENTARY SET THEORY Basic probability concepts can be cast in
More informationIntroducing Proof 1. hsn.uk.net. Contents
Contents 1 1 Introduction 1 What is proof? 1 Statements, Definitions and Euler Diagrams 1 Statements 1 Definitions Our first proof Euler diagrams 4 3 Logical Connectives 5 Negation 6 Conjunction 7 Disjunction
More informationCMSC Discrete Mathematics SOLUTIONS TO FIRST MIDTERM EXAM October 18, 2005 posted Nov 2, 2005
CMSC-37110 Discrete Mathematics SOLUTIONS TO FIRST MIDTERM EXAM October 18, 2005 posted Nov 2, 2005 Instructor: László Babai Ryerson 164 e-mail: laci@cs This exam contributes 20% to your course grade.
More informationMathematics 114L Spring 2018 D.A. Martin. Mathematical Logic
Mathematics 114L Spring 2018 D.A. Martin Mathematical Logic 1 First-Order Languages. Symbols. All first-order languages we consider will have the following symbols: (i) variables v 1, v 2, v 3,... ; (ii)
More informationDR.RUPNATHJI( DR.RUPAK NATH )
Contents 1 Sets 1 2 The Real Numbers 9 3 Sequences 29 4 Series 59 5 Functions 81 6 Power Series 105 7 The elementary functions 111 Chapter 1 Sets It is very convenient to introduce some notation and terminology
More informationCS 124 Math Review Section January 29, 2018
CS 124 Math Review Section CS 124 is more math intensive than most of the introductory courses in the department. You re going to need to be able to do two things: 1. Perform some clever calculations to
More informationCosets and Lagrange s theorem
Cosets and Lagrange s theorem These are notes on cosets and Lagrange s theorem some of which may already have been lecturer. There are some questions for you included in the text. You should write the
More informationCMPSCI 601: Tarski s Truth Definition Lecture 15. where
@ CMPSCI 601: Tarski s Truth Definition Lecture 15! "$#&%(') *+,-!".#/%0'!12 43 5 6 7 8:9 4; 9 9 < = 9 = or 5 6?>A@B!9 2 D for all C @B 9 CFE where ) CGE @B-HI LJKK MKK )HG if H ; C if H @ 1 > > > Fitch
More informationDiscrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Note 16. Random Variables: Distribution and Expectation
CS 70 Discrete Mathematics and Probability Theory Spring 206 Rao and Walrand Note 6 Random Variables: Distribution and Expectation Example: Coin Flips Recall our setup of a probabilistic experiment as
More information1 Basic Combinatorics
1 Basic Combinatorics 1.1 Sets and sequences Sets. A set is an unordered collection of distinct objects. The objects are called elements of the set. We use braces to denote a set, for example, the set
More informationProbability (Devore Chapter Two)
Probability (Devore Chapter Two) 1016-345-01: Probability and Statistics for Engineers Fall 2012 Contents 0 Administrata 2 0.1 Outline....................................... 3 1 Axiomatic Probability 3
More informationThis section is an introduction to the basic themes of the course.
Chapter 1 Matrices and Graphs 1.1 The Adjacency Matrix This section is an introduction to the basic themes of the course. Definition 1.1.1. A simple undirected graph G = (V, E) consists of a non-empty
More informationDiscrete Mathematics and Probability Theory Fall 2012 Vazirani Note 14. Random Variables: Distribution and Expectation
CS 70 Discrete Mathematics and Probability Theory Fall 202 Vazirani Note 4 Random Variables: Distribution and Expectation Random Variables Question: The homeworks of 20 students are collected in, randomly
More informationLöwenheim-Skolem Theorems, Countable Approximations, and L ω. David W. Kueker (Lecture Notes, Fall 2007)
Löwenheim-Skolem Theorems, Countable Approximations, and L ω 0. Introduction David W. Kueker (Lecture Notes, Fall 2007) In its simplest form the Löwenheim-Skolem Theorem for L ω1 ω states that if σ L ω1
More informationGraph coloring, perfect graphs
Lecture 5 (05.04.2013) Graph coloring, perfect graphs Scribe: Tomasz Kociumaka Lecturer: Marcin Pilipczuk 1 Introduction to graph coloring Definition 1. Let G be a simple undirected graph and k a positive
More informationRamsey theory of homogeneous structures
Ramsey theory of homogeneous structures Natasha Dobrinen University of Denver Notre Dame Logic Seminar September 4, 2018 Dobrinen big Ramsey degrees University of Denver 1 / 79 Ramsey s Theorem for Pairs
More informationLecture 3: Sizes of Infinity
Math/CS 20: Intro. to Math Professor: Padraic Bartlett Lecture 3: Sizes of Infinity Week 2 UCSB 204 Sizes of Infinity On one hand, we know that the real numbers contain more elements than the rational
More informationNotes. Combinatorics. Combinatorics II. Notes. Notes. Slides by Christopher M. Bourke Instructor: Berthe Y. Choueiry. Spring 2006
Combinatorics Slides by Christopher M. Bourke Instructor: Berthe Y. Choueiry Spring 2006 Computer Science & Engineering 235 Introduction to Discrete Mathematics Sections 4.1-4.6 & 6.5-6.6 of Rosen cse235@cse.unl.edu
More informationModule 1. Probability
Module 1 Probability 1. Introduction In our daily life we come across many processes whose nature cannot be predicted in advance. Such processes are referred to as random processes. The only way to derive
More informationBasics of Model Theory
Chapter udf Basics of Model Theory bas.1 Reducts and Expansions mod:bas:red: defn:reduct mod:bas:red: prop:reduct Often it is useful or necessary to compare languages which have symbols in common, as well
More informationSets, Models and Proofs. I. Moerdijk and J. van Oosten Department of Mathematics Utrecht University
Sets, Models and Proofs I. Moerdijk and J. van Oosten Department of Mathematics Utrecht University 2000; revised, 2006 Contents 1 Sets 1 1.1 Cardinal Numbers........................ 2 1.1.1 The Continuum
More informationDiscrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 10
EECS 70 Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 10 Introduction to Basic Discrete Probability In the last note we considered the probabilistic experiment where we flipped
More informationDiscrete Mathematics for CS Spring 2006 Vazirani Lecture 22
CS 70 Discrete Mathematics for CS Spring 2006 Vazirani Lecture 22 Random Variables and Expectation Question: The homeworks of 20 students are collected in, randomly shuffled and returned to the students.
More information3 The language of proof
3 The language of proof After working through this section, you should be able to: (a) understand what is asserted by various types of mathematical statements, in particular implications and equivalences;
More informationMA554 Assessment 1 Cosets and Lagrange s theorem
MA554 Assessment 1 Cosets and Lagrange s theorem These are notes on cosets and Lagrange s theorem; they go over some material from the lectures again, and they have some new material it is all examinable,
More informationMTH 202 : Probability and Statistics
MTH 202 : Probability and Statistics Lecture 5-8 : 15, 20, 21, 23 January, 2013 Random Variables and their Probability Distributions 3.1 : Random Variables Often while we need to deal with probability
More informationBasic Probability. Introduction
Basic Probability Introduction The world is an uncertain place. Making predictions about something as seemingly mundane as tomorrow s weather, for example, is actually quite a difficult task. Even with
More informationFebruary 2017 February 18, 2017
February 017 February 18, 017 Combinatorics 1. Kelvin the Frog is going to roll three fair ten-sided dice with faces labelled 0, 1,,..., 9. First he rolls two dice, and finds the sum of the two rolls.
More information* 8 Groups, with Appendix containing Rings and Fields.
* 8 Groups, with Appendix containing Rings and Fields Binary Operations Definition We say that is a binary operation on a set S if, and only if, a, b, a b S Implicit in this definition is the idea that
More informationLogic: The Big Picture
Logic: The Big Picture A typical logic is described in terms of syntax: what are the legitimate formulas semantics: under what circumstances is a formula true proof theory/ axiomatization: rules for proving
More informationPart II. Logic and Set Theory. Year
Part II Year 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2018 60 Paper 4, Section II 16G State and prove the ǫ-recursion Theorem. [You may assume the Principle of ǫ- Induction.]
More informationDiscrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 3
EECS 70 Discrete Mathematics and Probability Theory Spring 014 Anant Sahai Note 3 Induction Induction is an extremely powerful tool in mathematics. It is a way of proving propositions that hold for all
More informationOverview of Topics. Finite Model Theory. Finite Model Theory. Connections to Database Theory. Qing Wang
Overview of Topics Finite Model Theory Part 1: Introduction 1 What is finite model theory? 2 Connections to some areas in CS Qing Wang qing.wang@anu.edu.au Database theory Complexity theory 3 Basic definitions
More information0-1 Laws for Fragments of SOL
0-1 Laws for Fragments of SOL Haggai Eran Iddo Bentov Project in Logical Methods in Combinatorics course Winter 2010 Outline 1 Introduction Introduction Prefix Classes Connection between the 0-1 Law and
More informationMathematical Logic (IX)
Mathematical Logic (IX) Yijia Chen 1. The Löwenheim-Skolem Theorem and the Compactness Theorem Using the term-interpretation, it is routine to verify: Theorem 1.1 (Löwenheim-Skolem). Let Φ L S be at most
More informationDiscrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Note 14
CS 70 Discrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Note 14 Introduction One of the key properties of coin flips is independence: if you flip a fair coin ten times and get ten
More informationTheorem (Special Case of Ramsey s Theorem) R(k, l) is finite. Furthermore, it satisfies,
Math 16A Notes, Wee 6 Scribe: Jesse Benavides Disclaimer: These notes are not nearly as polished (and quite possibly not nearly as correct) as a published paper. Please use them at your own ris. 1. Ramsey
More informationReverse mathematics of some topics from algorithmic graph theory
F U N D A M E N T A MATHEMATICAE 157 (1998) Reverse mathematics of some topics from algorithmic graph theory by Peter G. C l o t e (Chestnut Hill, Mass.) and Jeffry L. H i r s t (Boone, N.C.) Abstract.
More informationProof Techniques (Review of Math 271)
Chapter 2 Proof Techniques (Review of Math 271) 2.1 Overview This chapter reviews proof techniques that were probably introduced in Math 271 and that may also have been used in a different way in Phil
More informationThe random graph. Peter J. Cameron University of St Andrews Encontro Nacional da SPM Caparica, 14 da julho 2014
The random graph Peter J. Cameron University of St Andrews Encontro Nacional da SPM Caparica, 14 da julho 2014 The random graph The countable random graph is one of the most extraordinary objects in mathematics.
More informationDiscrete Mathematics and Probability Theory Fall 2013 Vazirani Note 1
CS 70 Discrete Mathematics and Probability Theory Fall 013 Vazirani Note 1 Induction Induction is a basic, powerful and widely used proof technique. It is one of the most common techniques for analyzing
More informationChapter 2. Mathematical Reasoning. 2.1 Mathematical Models
Contents Mathematical Reasoning 3.1 Mathematical Models........................... 3. Mathematical Proof............................ 4..1 Structure of Proofs........................ 4.. Direct Method..........................
More informationWe are going to discuss what it means for a sequence to converge in three stages: First, we define what it means for a sequence to converge to zero
Chapter Limits of Sequences Calculus Student: lim s n = 0 means the s n are getting closer and closer to zero but never gets there. Instructor: ARGHHHHH! Exercise. Think of a better response for the instructor.
More informationEFRON S COINS AND THE LINIAL ARRANGEMENT
EFRON S COINS AND THE LINIAL ARRANGEMENT To (Richard P. S. ) 2 Abstract. We characterize the tournaments that are dominance graphs of sets of (unfair) coins in which each coin displays its larger side
More informationRao s degree sequence conjecture
Rao s degree sequence conjecture Maria Chudnovsky 1 Columbia University, New York, NY 10027 Paul Seymour 2 Princeton University, Princeton, NJ 08544 July 31, 2009; revised December 10, 2013 1 Supported
More informationTheorem. For every positive integer n, the sum of the positive integers from 1 to n is n(n+1)
Week 1: Logic Lecture 1, 8/1 (Sections 1.1 and 1.3) Examples of theorems and proofs Theorem (Pythagoras). Let ABC be a right triangle, with legs of lengths a and b, and hypotenuse of length c. Then a +
More informationEconomics 204 Fall 2011 Problem Set 1 Suggested Solutions
Economics 204 Fall 2011 Problem Set 1 Suggested Solutions 1. Suppose k is a positive integer. Use induction to prove the following two statements. (a) For all n N 0, the inequality (k 2 + n)! k 2n holds.
More informationStatistics 1 - Lecture Notes Chapter 1
Statistics 1 - Lecture Notes Chapter 1 Caio Ibsen Graduate School of Economics - Getulio Vargas Foundation April 28, 2009 We want to establish a formal mathematic theory to work with results of experiments
More informationChapter One. The Real Number System
Chapter One. The Real Number System We shall give a quick introduction to the real number system. It is imperative that we know how the set of real numbers behaves in the way that its completeness and
More informationApplications of the Lopsided Lovász Local Lemma Regarding Hypergraphs
Regarding Hypergraphs Ph.D. Dissertation Defense April 15, 2013 Overview The Local Lemmata 2-Coloring Hypergraphs with the Original Local Lemma Counting Derangements with the Lopsided Local Lemma Lopsided
More informationRamsey Theory. May 24, 2015
Ramsey Theory May 24, 2015 1 König s Lemma König s Lemma is a basic tool to move between finite and infinite combinatorics. To be concise, we use the notation [k] = {1, 2,..., k}, and [X] r will denote
More informationADVANCED CALCULUS - MTH433 LECTURE 4 - FINITE AND INFINITE SETS
ADVANCED CALCULUS - MTH433 LECTURE 4 - FINITE AND INFINITE SETS 1. Cardinal number of a set The cardinal number (or simply cardinal) of a set is a generalization of the concept of the number of elements
More informationAcyclic subgraphs with high chromatic number
Acyclic subgraphs with high chromatic number Safwat Nassar Raphael Yuster Abstract For an oriented graph G, let f(g) denote the maximum chromatic number of an acyclic subgraph of G. Let f(n) be the smallest
More informationExample. Lemma. Proof Sketch. 1 let A be a formula that expresses that node t is reachable from s
Summary Summary Last Lecture Computational Logic Π 1 Γ, x : σ M : τ Γ λxm : σ τ Γ (λxm)n : τ Π 2 Γ N : τ = Π 1 [x\π 2 ] Γ M[x := N] Georg Moser Institute of Computer Science @ UIBK Winter 2012 the proof
More informationConnectedness. Proposition 2.2. The following are equivalent for a topological space (X, T ).
Connectedness 1 Motivation Connectedness is the sort of topological property that students love. Its definition is intuitive and easy to understand, and it is a powerful tool in proofs of well-known results.
More informationWe have seen that the symbols,,, and can guide the logical
CHAPTER 7 Quantified Statements We have seen that the symbols,,, and can guide the logical flow of algorithms. We have learned how to use them to deconstruct many English sentences into a symbolic form.
More informationProbability theory basics
Probability theory basics Michael Franke Basics of probability theory: axiomatic definition, interpretation, joint distributions, marginalization, conditional probability & Bayes rule. Random variables:
More informationContribution of Problems
Exam topics 1. Basic structures: sets, lists, functions (a) Sets { }: write all elements, or define by condition (b) Set operations: A B, A B, A\B, A c (c) Lists ( ): Cartesian product A B (d) Functions
More informationGRAPHIC REALIZATIONS OF SEQUENCES. Under the direction of Dr. John S. Caughman
GRAPHIC REALIZATIONS OF SEQUENCES JOSEPH RICHARDS Under the direction of Dr. John S. Caughman A Math 501 Project Submitted in partial fulfillment of the requirements for the degree of Master of Science
More informationSergey Norin Department of Mathematics and Statistics McGill University Montreal, Quebec H3A 2K6, Canada. and
NON-PLANAR EXTENSIONS OF SUBDIVISIONS OF PLANAR GRAPHS Sergey Norin Department of Mathematics and Statistics McGill University Montreal, Quebec H3A 2K6, Canada and Robin Thomas 1 School of Mathematics
More informationNear-domination in graphs
Near-domination in graphs Bruce Reed Researcher, Projet COATI, INRIA and Laboratoire I3S, CNRS France, and Visiting Researcher, IMPA, Brazil Alex Scott Mathematical Institute, University of Oxford, Oxford
More informationCompleteness in the Monadic Predicate Calculus. We have a system of eight rules of proof. Let's list them:
Completeness in the Monadic Predicate Calculus We have a system of eight rules of proof. Let's list them: PI At any stage of a derivation, you may write down a sentence φ with {φ} as its premiss set. TC
More informationExercises for Unit VI (Infinite constructions in set theory)
Exercises for Unit VI (Infinite constructions in set theory) VI.1 : Indexed families and set theoretic operations (Halmos, 4, 8 9; Lipschutz, 5.3 5.4) Lipschutz : 5.3 5.6, 5.29 5.32, 9.14 1. Generalize
More informationcse547, math547 DISCRETE MATHEMATICS Professor Anita Wasilewska
cse547, math547 DISCRETE MATHEMATICS Professor Anita Wasilewska LECTURE 1 INTRODUCTION Course Web Page www.cs.stonybrook.edu/ cse547 The webpage contains: detailed lectures notes slides; very detailed
More information(Refer Slide Time: 0:21)
Theory of Computation Prof. Somenath Biswas Department of Computer Science and Engineering Indian Institute of Technology Kanpur Lecture 7 A generalisation of pumping lemma, Non-deterministic finite automata
More informationDirect Proof and Counterexample I:Introduction
Direct Proof and Counterexample I:Introduction Copyright Cengage Learning. All rights reserved. Goal Importance of proof Building up logic thinking and reasoning reading/using definition interpreting :
More informationThe semantics of propositional logic
The semantics of propositional logic Readings: Sections 1.3 and 1.4 of Huth and Ryan. In this module, we will nail down the formal definition of a logical formula, and describe the semantics of propositional
More informationCS261: A Second Course in Algorithms Lecture #18: Five Essential Tools for the Analysis of Randomized Algorithms
CS261: A Second Course in Algorithms Lecture #18: Five Essential Tools for the Analysis of Randomized Algorithms Tim Roughgarden March 3, 2016 1 Preamble In CS109 and CS161, you learned some tricks of
More informationCombinatorial Optimization
Combinatorial Optimization Problem set 8: solutions 1. Fix constants a R and b > 1. For n N, let f(n) = n a and g(n) = b n. Prove that f(n) = o ( g(n) ). Solution. First we observe that g(n) 0 for all
More informationA prolific construction of strongly regular graphs with the n-e.c. property
A prolific construction of strongly regular graphs with the n-e.c. property Peter J. Cameron and Dudley Stark School of Mathematical Sciences Queen Mary, University of London London E1 4NS, U.K. Abstract
More informationDirect Proof and Counterexample I:Introduction. Copyright Cengage Learning. All rights reserved.
Direct Proof and Counterexample I:Introduction Copyright Cengage Learning. All rights reserved. Goal Importance of proof Building up logic thinking and reasoning reading/using definition interpreting statement:
More informationNotes 6 : First and second moment methods
Notes 6 : First and second moment methods Math 733-734: Theory of Probability Lecturer: Sebastien Roch References: [Roc, Sections 2.1-2.3]. Recall: THM 6.1 (Markov s inequality) Let X be a non-negative
More informationMATH 2200 Final LC Review
MATH 2200 Final LC Review Thomas Goller April 25, 2013 1 Final LC Format The final learning celebration will consist of 12-15 claims to be proven or disproven. It will take place on Wednesday, May 1, from
More informationDiscrete Structures for Computer Science: Counting, Recursion, and Probability
Discrete Structures for Computer Science: Counting, Recursion, and Probability Michiel Smid School of Computer Science Carleton University Ottawa, Ontario Canada michiel@scs.carleton.ca December 18, 2017
More informationReview 1. Andreas Klappenecker
Review 1 Andreas Klappenecker Summary Propositional Logic, Chapter 1 Predicate Logic, Chapter 1 Proofs, Chapter 1 Sets, Chapter 2 Functions, Chapter 2 Sequences and Sums, Chapter 2 Asymptotic Notations,
More informationMI 4 Mathematical Induction Name. Mathematical Induction
Mathematical Induction It turns out that the most efficient solution to the Towers of Hanoi problem with n disks takes n 1 moves. If this isn t the formula you determined, make sure to check your data
More informationGraph Theory. Thomas Bloom. February 6, 2015
Graph Theory Thomas Bloom February 6, 2015 1 Lecture 1 Introduction A graph (for the purposes of these lectures) is a finite set of vertices, some of which are connected by a single edge. Most importantly,
More informationLecture Notes on DISCRETE MATHEMATICS. Eusebius Doedel
Lecture Notes on DISCRETE MATHEMATICS Eusebius Doedel c Eusebius J. Doedel, 009 Contents Logic. Introduction............................................................................... Basic logical
More informationMATH 2200 Final Review
MATH 00 Final Review Thomas Goller December 7, 01 1 Exam Format The final exam will consist of 8-10 proofs It will take place on Tuesday, December 11, from 10:30 AM - 1:30 PM, in the usual room Topics
More informationLaplacian Integral Graphs with Maximum Degree 3
Laplacian Integral Graphs with Maximum Degree Steve Kirkland Department of Mathematics and Statistics University of Regina Regina, Saskatchewan, Canada S4S 0A kirkland@math.uregina.ca Submitted: Nov 5,
More informationConditional Probability
Conditional Probability When we obtain additional information about a probability experiment, we want to use the additional information to reassess the probabilities of events given the new information.
More information