CSM Workshop 1: Zeros of Graph Polynomials Enumeration of Spanning Subgraphs with Degree Constraints Dave Wagner University of Waterloo.

Slides:



Advertisements
Similar presentations
Vector Spaces A set V is called a vector space over a set K denoted V(K) if is an Abelian group, is a field, and For every element vV and K there exists.
Advertisements

Multicoloring Unit Disk Graphs on Triangular Lattice Points Yuichiro MIYAMOTO Sophia University Tomomi MATSUI University of Tokyo.
Covers, Dominations, Independent Sets and Matchings AmirHossein Bayegan Amirkabir University of Technology.
On the Density of a Graph and its Blowup Raphael Yuster Joint work with Asaf Shapira.
Totally Unimodular Matrices
Graphs III (Trees, MSTs) (Chp 11.5, 11.6)
Linear Equations in Linear Algebra
Combinatorial Algorithms
C++ Programming: Program Design Including Data Structures, Third Edition Chapter 21: Graphs.
Graph Sparsifiers by Edge-Connectivity and Random Spanning Trees Nick Harvey University of Waterloo Department of Combinatorics and Optimization Joint.
Graph Sparsifiers by Edge-Connectivity and Random Spanning Trees Nick Harvey U. Waterloo C&O Joint work with Isaac Fung TexPoint fonts used in EMF. Read.
What is the next line of the proof? a). Let G be a graph with k vertices. b). Assume the theorem holds for all graphs with k+1 vertices. c). Let G be a.
CS5371 Theory of Computation
Is the following graph Hamiltonian- connected from vertex v? a). Yes b). No c). I have absolutely no idea v.
Matching Polytope, Stable Matching Polytope Lecture 8: Feb 2 x1 x2 x3 x1 x2 x3.
Expanders Eliyahu Kiperwasser. What is it? Expanders are graphs with no small cuts. The later gives several unique traits to such graph, such as: – High.
CTIS 154 Discrete Mathematics II1 8.2 Paths and Cycles Kadir A. Peker.
Coloring Algorithms and Networks. Coloring2 Graph coloring Vertex coloring: –Function f: V  C, such that for all {v,w}  E: f(v)  f(w) Chromatic number.
What is the next line of the proof? a). Assume the theorem holds for all graphs with k edges. b). Let G be a graph with k edges. c). Assume the theorem.
The (Degree, Diameter) Problem By Whitney Sherman.
MATH 310, FALL 2003 (Combinatorial Problem Solving) Lecture 10, Monday, September 22.
Chapter 3 Limits and the Derivative
1 Preliminaries Precalculus Review I Precalculus Review II
Inequalities and Proof
Algebra of Limits Assume that both of the following limits exist and c and is a real number: Then:
Percolation in self-similar networks Dmitri Krioukov CAIDA/UCSD M. Á. Serrano, M. Boguñá UNT, March 2011.
MCS 312: NP Completeness and Approximation algorithms Instructor Neelima Gupta
Algebra of Limits Assume that both of the following limits exist and c and is a real number: Then:
7.1 and 7.2: Spanning Trees. A network is a graph that is connected –The network must be a sub-graph of the original graph (its edges must come from the.
Edge-disjoint induced subgraphs with given minimum degree Raphael Yuster 2012.
Chapter 2 Mathematical preliminaries 2.1 Set, Relation and Functions 2.2 Proof Methods 2.3 Logarithms 2.4 Floor and Ceiling Functions 2.5 Factorial and.
1 CS104 : Discrete Structures Chapter V Graph Theory.
Maximum density of copies of a graph in the n-cube John Goldwasser Ryan Hansen West Virginia University.
Testing the independence number of hypergraphs
Introduction to Graph Theory
The countable character of uncountable graphs François Laviolette Barbados 2003.
10. Lecture WS 2006/07Bioinformatics III1 V10: Network Flows V10 follows closely chapter 12.1 in on „Flows and Cuts in Networks and Chapter 12.2 on “Solving.
Max Flow – Min Cut Problem. Directed Graph Applications Shortest Path Problem (Shortest path from one point to another) Max Flow problems (Maximum material.
Instructor Neelima Gupta Table of Contents Class NP Class NPC Approximation Algorithms.
PHY 301: MATH AND NUM TECH Contents Chapter 7: Complex Numbers I.Definitions and Representation II.Properties III.Differentiaton of complex functions.
1.1 Chapter 3: Proving NP-completeness Results Six Basic NP-Complete Problems Some Techniques for Proving NP-Completeness Some Suggested Exercises.
FINDING A POLYNOMIAL PASSING THROUGH A POINT. Review: the Linear Factorization Theorem If where n > 1 and a n ≠ 0 then Where c 1, c 2, … c n are complex.
Dense graphs with a large triangle cover have a large triangle packing Raphael Yuster SIAM DM’10.
Signal & Weight Vector Spaces
Week 4 Functions and Graphs. Objectives At the end of this session, you will be able to: Define and compute slope of a line. Write the point-slope equation.
8.4 Closures of Relations Definition: The closure of a relation R with respect to property P is the relation obtained by adding the minimum number of.
Chapter 11 Introduction to Computational Complexity Copyright © 2011 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1.
Some Favorite Problems Dan Kleitman, M.I.T.. The Hirsch Conjecture 1. How large can the diameter of a bounded polytope defined by n linear constraints.
Section 4.1 Polynomial Functions and Models.
Approximation Algorithms based on linear programming.
Sect. 4.5: Cayley-Klein Parameters 3 independent quantities are needed to specify a rigid body orientation. Most often, we choose them to be the Euler.
CHAPTER SIX T HE P ROBABILISTIC M ETHOD M1 Zhang Cong 2011/Nov/28.
Trees.
The countable character of uncountable graphs François Laviolette Barbados 2003.
Algebra II Explorations Review ( )
Graphing Equations and Inequalities
3.8 Complex Zeros; Fundamental Theorem of Algebra
4.1 More Nonlinear Functions and Their Graphs
Vertex Covers, Matchings, and Independent Sets
Problem Solving 4.
Fundamental Theorem of Algebra
8.4 Vectors.
Algorithms (2IL15) – Lecture 7
Chapter 15 Graph Theory © 2008 Pearson Addison-Wesley.
4.1 More Nonlinear Functions and Their Graphs
More Nonlinear Functions and Equations
Locality In Distributed Graph Algorithms
Integer and fractional packing of graph families
Vertex Covers and Matchings
Lecture 24 Vertex Cover and Hamiltonian Cycle
Presentation transcript:

CSM Workshop 1: Zeros of Graph Polynomials Enumeration of Spanning Subgraphs with Degree Constraints Dave Wagner University of Waterloo

I. The Set-Up

graph notation G=(V,E) a (big) finite graph

graph notation G=(V,E) a (big) finite graph a set of edges, i.e. a spanning subgraph

graph notation G=(V,E) a (big) finite graph a set of edges, i.e. a spanning subgraph the degree function of H

graph notation G=(V,E) a (big) finite graph a set of edges, i.e. a spanning subgraph the degree function of H the set of vertices of degree k in H is

energy of a subgraph J the energy of a single edge

energy of a subgraph J the energy of a single edge the “chemical potential” of a vertex of degree k

energy of a subgraph J the energy of a single edge the “chemical potential” of a vertex of degree k the energy of a (spanning) subgraph H is

partition function T the absolute temperature

partition function T the absolute temperature the inverse temperature

partition function T the absolute temperature the inverse temperature the Boltzmann weight of a subgraph H is

partition function T the absolute temperature the inverse temperature the Boltzmann weight of a subgraph H is the partition function is

polynomial expression let and

polynomial expression let and for a subgraph H let

polynomial expression let and for a subgraph H let the partition function is

multivariate version let and

multivariate version let and the multivariate partition function is

multivariate version let and the multivariate partition function is then

example let and for all k>=2

example let and for all k>=2

example let and for all k>=2 and are, respectively, the multivariate and univariate matching polynomials of G

vertex-dependent activities the chemical potentials can vary from vertex to vertex:

vertex-dependent activities the chemical potentials can vary from vertex to vertex: let where

vertex-dependent activities the chemical potentials can vary from vertex to vertex: let where and redefine

vertex-dependent activities the chemical potentials can vary from vertex to vertex: let where and redefine the multivariate partition function is still

II. The Results

the key polynomials for each vertex v of G form the key polynomial in which

the key polynomials for each vertex v of G form the key polynomial in which Since this polynomial depends on T

the key polynomials for each vertex v of G form the key polynomial in which Since this polynomial depends on T except when all

the key polynomials for each vertex v of G form the key polynomial in which Since this polynomial depends on T except when all that is, when all

first theorem Assume that all zeros of all the keys are within an angle of the negative real axis Then…

first theorem Assume that all zeros of all the keys are within an angle of the negative real axis Then… 1. If for all v then

first theorem Assume that all zeros of all the keys are within an angle of the negative real axis Then… 1. If for all v then 2. If then

first theorem Assume that all zeros of all the keys are within an angle of the negative real axis Then… 1. If for all v then 2. If then This statement is independent of the size of the graph….

first theorem Assume that all zeros of all the keys are within an angle of the negative real axis Then… 1. If for all v then 2. If then This statement is independent of the size of the graph…. so it can be used for thermodynamic limits.

first theorem

Consider the case

first theorem Consider the case Assume that all zeros of all the keys are nonpositive real numbers. Then…

first theorem Consider the case Assume that all zeros of all the keys are nonpositive real numbers. Then… 1. If for all v then

first theorem Consider the case Assume that all zeros of all the keys are nonpositive real numbers. Then… 1. If for all v then (the half-plane property)

first theorem Consider the case Assume that all zeros of all the keys are nonpositive real numbers. Then… 1. If for all v then (the half-plane property) 2. All zeros of are nonpositive real numbers.

the Heilmann-Lieb (1972) theorem let and for all k>=2

the Heilmann-Lieb (1972) theorem let and for all k>=2 for each vertex v, has only real nonpositive zeros….

the Heilmann-Lieb (1972) theorem let and for all k>=2 for each vertex v, has only real nonpositive zeros.… 1. The multivariate matching polynomial has the half-plane property.

the Heilmann-Lieb (1972) theorem let and for all k>=2 for each vertex v, has only real nonpositive zeros…. 1. The multivariate matching polynomial has the half-plane property. 2. The univariate matching polynomial has only real nonpositive zeros.

a generalization fix functions such that (at every vertex)

a generalization fix functions such that (at every vertex) choose vertex chemical potentials so that

a generalization fix functions such that (at every vertex) choose vertex chemical potentials so that Then every key has only real nonpositive zeros, so that 1. has the half-plane property (new) 2. has only real nonpositive zeros (W. 1996)

a theorem of Ruelle (1999) let and for all k>=3

a theorem of Ruelle (1999) let and for all k>=3 for each vertex v, has all its zeros within of the negative real axis

a theorem of Ruelle (1999) let and for all k>=3 for each vertex v, has all its zeros within of the negative real axis 1. If for all v then (new)

a theorem of Ruelle (1999) let and for all k>=3 for each vertex v, has all its zeros within of the negative real axis 1. If for all v then (new) 2. If then

a theorem of Ruelle (1999) let and for all k>=3 for each vertex v, has all its zeros within of the negative real axis 1. If for all v then (new) 2. If then (Ruelle proves that for 2. it suffices that for a graph with maximum degree.)

second theorem Assume that all zeros of all the keys have modulus at least. Then… 1. If for all v then 2. If then

third theorem Assume that all zeros of all the keys have modulus at most, and that the degree of each key equals the degree of the corresponding vertex. Then… 1. If for all v then 2. If then

corollary If all zeros of all keys are on the unit circle, and all keys have the same degree as the corresponding vertex, then every zero of is on the unit circle.

corollary If all zeros of all keys are on the unit circle, and all keys have the same degree as the corresponding vertex, then every zero of is on the unit circle. For any graph G, every zero of is on the unit circle.

application consider a sequence of graphs G whose union is an infinite graph

application consider a sequence of graphs G whose union is an infinite graph assume that each graph G is d-regular

application consider a sequence of graphs G whose union is an infinite graph assume that each graph G is d-regular that all keys are the same

application consider a sequence of graphs G whose union is an infinite graph assume that each graph G is d-regular that all keys are the same and that the thermodynamic limit free energy exists:

application consider a sequence of graphs G whose union is an infinite graph assume that each graph G is d-regular that all keys are the same and that the thermodynamic limit free energy exists: If the free energy is non-analytic at a nonnegative real then has a zero not at the origin with nonnegative real part.

example 1. let and for all k>=3

example 1. let and for all k>=3 the key is

example 1. let and for all k>=3 the key is if then the zeros of K(z) have negative real part…. No phase transitions for any physical (J,T)

example 1. let and for all k>=3 the key is if then the zeros of K(z) have negative real part…. No phase transitions for any physical (J,T) from the second theorem it follows that when there is no phase transition for

example 2. fix functions such that (at every vertex)

example 2. fix functions such that (at every vertex) choose vertex chemical potentials so that

example 2. fix functions such that (at every vertex) choose vertex chemical potentials so that When the thermodynamic limit exists it is analytic for all physical values of (J,T). (no phase transitions)

example 3. in a 2d-regular graph, consider the key

example 3. in a 2d-regular graph, consider the key for a thermodynamic limit of these a phase transition with can only happen at

III. Summary

summary * very general set-up, but it records no global structure

summary * very general set-up, but it records no global structure * unifies a number of previously considered things

summary * very general set-up, but it records no global structure * unifies a number of previously considered things * very mild hypotheses, but similarly weak conclusions about absence of phase transitions:

summary * very general set-up, but it records no global structure * unifies a number of previously considered things * very mild hypotheses, but similarly weak conclusions about absence of phase transitions: * many general “soft” results

summary * very general set-up, but it records no global structure * unifies a number of previously considered things * very mild hypotheses, but similarly weak conclusions about absence of phase transitions: * many general “soft” results * some quantitative “hard” versions of qualitatively intuitive results

summary * very general set-up, but it records no global structure * unifies a number of previously considered things * very mild hypotheses, but similarly weak conclusions about absence of phase transitions: * many general “soft” results * some quantitative “hard” versions of qualitatively intuitive results * proofs are short and easy: (half-plane property/polarize & Grace-Walsh-Szego/ “monkey business”/diagonalize)