M-saturated M={ } M-unsaturated. Perfect Matching. Matchings

Similar documents
Graph G = (V, E). V ={vertices}, E={edges}. V={a,b,c,d,e,f,g,h,k} E={(a,b),(a,g),( a,h),(a,k),(b,c),(b,k),...,(h,k)}

Recall: Matchings. Examples. K n,m, K n, Petersen graph, Q k ; graphs without perfect matching

List of Theorems. Mat 416, Introduction to Graph Theory. Theorem 1 The numbers R(p, q) exist and for p, q 2,

Constructive proof of deficiency theorem of (g, f)-factor

1 Matchings in Non-Bipartite Graphs

Graphs & Algorithms: Advanced Topics Nowhere-Zero Flows

Introduction to Graph Theory

Notes on Graph Theory

15.1 Matching, Components, and Edge cover (Collaborate with Xin Yu)

GRAPH ALGORITHMS Week 7 (13 Nov - 18 Nov 2017)

7.5 Bipartite Matching

The Matching Polytope: General graphs

ACO Comprehensive Exam March 17 and 18, Computability, Complexity and Algorithms

Maximum flow problem

6.046 Recitation 11 Handout

Generalized Pigeonhole Properties of Graphs and Oriented Graphs

Chapter 7 Matchings and r-factors

Observation 4.1 G has a proper separation of order 0 if and only if G is disconnected.

Nowhere zero flow. Definition: A flow on a graph G = (V, E) is a pair (D, f) such that. 1. D is an orientation of G. 2. f is a function on E.

Combinatorial Optimization

arxiv: v1 [cs.dm] 24 Jan 2008

2 Matchings and 1-Factors

Observation 4.1 G has a proper separation of order 0 if and only if G is disconnected.

Generating p-extremal graphs

Reductions. Example 1

DECOMPOSITIONS OF MULTIGRAPHS INTO PARTS WITH THE SAME SIZE

A New Characterization of König-Egervary Graphs

Bipartite Matchings. Andreas Klappenecker

Ma/CS 6a Class 28: Latin Squares

Part V. Matchings. Matching. 19 Augmenting Paths for Matchings. 18 Bipartite Matching via Flows

Advanced Topics in Discrete Math: Graph Theory Fall 2010

Ma/CS 6b Class 3: Stable Matchings

Algorithm Design and Analysis

Ring Sums, Bridges and Fundamental Sets

An approximate version of Hadwiger s conjecture for claw-free graphs

Bichain graphs: geometric model and universal graphs

Trees. A tree is a graph which is. (a) Connected and. (b) has no cycles (acyclic).

Triangle-free graphs with no six-vertex induced path

Coloring. Basics. A k-coloring of a loopless graph G is a function f : V (G) S where S = k (often S = [k]).

Imprimitive symmetric graphs with cyclic blocks

Analogies and discrepancies between the vertex cover number and the weakly connected domination number of a graph

Discrete Mathematics

A characterization of graphs by codes from their incidence matrices

Out-colourings of Digraphs

On the Existence of a Second Hamilton Cycle in Hamiltonian Graphs With Symmetry

Math 301: Matchings in Graphs

A taste of perfect graphs

Independent Transversals in r-partite Graphs

1.3 Vertex Degrees. Vertex Degree for Undirected Graphs: Let G be an undirected. Vertex Degree for Digraphs: Let D be a digraph and y V (D).

Solutions to Exercises Chapter 10: Ramsey s Theorem

Graph homomorphism into an odd cycle

Alternating paths revisited II: restricted b-matchings in bipartite graphs

Algorithm Design and Analysis

On a list-coloring problem

Some Complexity Problems on Single Input Double Output Controllers

Partial characterizations of clique-perfect graphs II: diamond-free and Helly circular-arc graphs

ALL GRAPHS WITH PAIRED-DOMINATION NUMBER TWO LESS THAN THEIR ORDER. Włodzimierz Ulatowski

AALBORG UNIVERSITY. Total domination in partitioned graphs. Allan Frendrup, Preben Dahl Vestergaard and Anders Yeo

arxiv: v1 [math.co] 20 Oct 2018

Agenda. Soviet Rail Network, We ve done Greedy Method Divide and Conquer Dynamic Programming

Advanced Combinatorial Optimization September 22, Lecture 4

Cographs; chordal graphs and tree decompositions

Ma/CS 6a Class 28: Latin Squares

Math 5707: Graph Theory, Spring 2017 Midterm 3

Graph coloring, perfect graphs

CYCLE STRUCTURES IN GRAPHS. Angela K. Harris. A thesis submitted to the. University of Colorado Denver. in partial fulfillment

CS 138b Computer Algorithm Homework #14. Ling Li, February 23, Construct the level graph

MINIMALLY NON-PFAFFIAN GRAPHS

Claw-free Graphs. III. Sparse decomposition

A Lemma on the Minimal Counter-example of Frankl s Conjecture

Coloring Vertices and Edges of a Path by Nonempty Subsets of a Set

Factors in Graphs With Multiple Degree Constraints

Nowhere-zero 3-flows in triangularly connected graphs

C-Perfect K-Uniform Hypergraphs

Maximum Alliance-Free and Minimum Alliance-Cover Sets

Chapter 7. Network Flow. Slides by Kevin Wayne. Copyright 2005 Pearson-Addison Wesley. All rights reserved.

Parameterised Subgraph Counting Problems

Unmixed Graphs that are Domains

Graph Theory. Thomas Bloom. February 6, 2015

Nowhere-zero Unoriented Flows in Hamiltonian Graphs

Fundamental Algorithms 11

Parity Versions of 2-Connectedness

Discrete Mathematics. The edge spectrum of the saturation number for small paths

Zero-Sum Flows in Regular Graphs

Preliminaries. Graphs. E : set of edges (arcs) (Undirected) Graph : (i, j) = (j, i) (edges) V = {1, 2, 3, 4, 5}, E = {(1, 3), (3, 2), (2, 4)}

5 Flows and cuts in digraphs

Colourings of cubic graphs inducing isomorphic monochromatic subgraphs

and critical partial Latin squares.

Even Cycles in Hypergraphs.

arxiv: v1 [math.co] 1 Oct 2013

Partial characterizations of clique-perfect graphs I: subclasses of claw-free graphs

Claw-Free Graphs With Strongly Perfect Complements. Fractional and Integral Version.

Antoni Marczyk A NOTE ON ARBITRARILY VERTEX DECOMPOSABLE GRAPHS

CONSTRAINED PERCOLATION ON Z 2

Advanced Combinatorial Optimization September 24, Lecture 5

Packing and Covering Dense Graphs

Computational Models - Lecture 3 1

On the algorithmic aspects of Hedetniemi s conjecture

Bipartite Matchings and Stable Marriage

CSCE 551 Final Exam, Spring 2004 Answer Key

Transcription:

Matchings A matching M of a graph G = (V, E) is a set of edges, no two of which are incident to a common vertex. M-saturated M={ } M-unsaturated Perfect Matching 1

M-alternating path M not M M not M M c b d a e f (a,b,c,d,e,f) is an M-alternating path An M-alternating path joining 2 M-unsaturated vertices is called an M-augmenting path. 2

M is a maximum matching of G if no matching M has more edges. Theorem 1 M is a maximum matching iff M admits no M-augmenting paths. Proof Suppose M has an augmenting path P = (a 0, b 1, a 1,..., a k, b k+1 ) where e i = (a i 1, b i ) / M, 1 i k+1 and f i = (b i, a i ) M, 1 i k. replacements a 1 a 2 b 1 b 3 a 0 b 2 M = M {f 1, f 2,..., f k } + {e 1, e 2,..., e k+1 }. 3

M = M + 1. M is a matching For x V let d M (x) denote the degree of x in matching M, So d M (x) is 0 or 1. d M (x) = d M (x) x {a 0, b 1,..., b k+1 } d M (x) x {b 1,..., a k } d M (x) + 1 x {a 0, b k+1 } So if M has an augmenting path it is not maximum. 4

Suppose M is not a maximum matching and M > M. Consider H = G[M M ] where M M = (M \ M ) (M \ M) is the set of edges in exactly one of M, M. Maximum degree of H is 2, at most 1 edge from M or M. So H is a collection of vertex disjoint alternating paths and cycles. M M (a) (b) x x,y M-unsaturated y (c) (d) M > M implies that there is at least one path of type (d). Such a path is M-augmenting 5

Bipartite Graphs Let G = (A B, E) be a bipartite graph with bipartition A, B. For S A let N(S) = {b B : a S, (a, b) E}. ag replacements a 1 b 1 a 2 b 2 a 3 b 3 a 4 b 4 N(a 2, a 3 ) = {b 1, b 3, b 4 } Clearly, M A, B for any matching M of G. 6

Hall s Theorem Theorem 2 G contains a matching of size A iff N(S) S S A. (1) replacements a 1 b 1 a 2 b 2 a 3 b 3 a 4 b 4 N({a 1, a 2, a 3 }) = {b 1, b 2 } and so at most 2 of a 1, a 2, a 3 can be saturated by a matching. 7

Only if: Suppose M = {(a, φ(a)) : a A} saturates A. 1 φ (2) ε N(S) 2 S φ(4) +non-matching edges 3 φ (1) ε N(S) 4 φ(3) ε N(S) N(S) {φ(s) : s S} = S and so (1) holds. If: Let M = {(a, φ(a)) : a A } (A A) is a maximum matching. Suppose a 0 A is M-unsaturated. We show that (1) fails. 8

Let A 1 = {a A : such that a is reachable from a 0 by an M-alternating path.} B 1 = {b B : such that b is reachable from a 0 by an M-alternating path.} B 1 B 1 B 1 B 1 cements a 0 A1 A 1 A 1 A 1 No A 1 : B \ B 1 edges 9

B 1 is M-saturated else there exists an M- augmenting path. cements If a A 1 \ {a 0 } then φ(a) B 1. a 0 φ(a) a If b B 1 then φ 1 (b) A 1 \ {a 0 }. So cements N(A 1 ) B 1 B 1 = A 1 1. a 0 b φ 1 (b) So N(A 1 ) = A 1 1 and (1) fails to hold. 10

Marriage Theorem Theorem 3 Suppose G = (A B, E) is k-regular. (k 1) i.e. d G (v) = k for all v A B. Then G has a perfect matching. Proof and so A = B. k A = E = k B Suppose S A. Let m be the number of edges incident with S. Then k S = m k N(S). So (1) holds and there is a matching of size A i.e. a perfect matching. 11

Edge Covers A set of vertices X V is a covering of G = (V, E) if every edge of E contains at least one endpoint in X. { } is a covering Lemma 1 If X is a covering and M is a matching then X M. Proof Let M = {(a 1, b i ) : 1 i k}. Then X M since a i X or b i X for 1 i k and a 1,..., b k are distinct. 12

Konig s Theorem Let µ(g) be the maximum size of a matching. Let β(g) be the minimum size of a covering. Then µ(g) β(g). Theorem 4 If G is bipartite then µ(g) = β(g). Proof Let M be a maximum matching. Let S 0 be the M-unsaturated vertices of A. Let S S 0 be the A-vertices which are reachable from S by M-alternating paths. Let T be the M-neighbours of S \ S 0. 13

T T T T cements S 0 S0 S 0 S S S S Let X = (A \ S) T. X = M. T = S \ S 0. The remaining edges of M cover A \ S exactly once. X is a cover. There are no edges (x, y) where x S and y B \ T. Otherwise, since y is M-saturated (no M-augmenting paths) the M-neightbour of y would have to be in S, contradicting y / T. 14

Tutte s Theorem We now discuss arbitrary (i.e. non-bipartite) graphs. For S V we let o(g S) denote the number of components of odd cardinality in G S. Theorem 5 G has a perfect matching iff o(g S) S for all S V. (2) Proof graphs. We restrict our attention to simple 15

Only if: x Even Components a b y z Need to match x,y,z to a,b Suppose S = k and O 1, O 2,..., O k+1 are odd components of G S. In any perfect matching of G, at least one vertex x i of C i will have to be matched outside O i for i = 1, 2,..., k + 1. But then x 1, x 2,..., x k+1 will all have to be matched with S, which is impossible. 16

If: Suppose (2) holds and G has no perfect matching. Add edges until we have a graph G which satisfies G has no perfect matching. G + e has a perfect matching for all e / E(G ). Clearly, o(g S) o(g S) S for all S V. (3) In particular, if S =, o(g ) = 0 and V is even. U = {v V : d G (v) = ν 1}. U V else G has a perfect matching. 17

Claim: G U is the disjoint union of complete graphs. Suppose C is a component of G U which is not a clique. Then there exist x, y, z C such that xy, xz E(G ) and xz / E(G ). Take x, z C at distance 2 in G. y w x z y / U implies that there exists w / U with yw / E(G ). Let M 1, M 2 be perfect matchings in G +xz, G + yw respectively. 18

Let H = M 1 M 2. H is a collection of vertex disjoint even cycles. Case 1: xz, yw are in different cycles of H. + + y w + + + x z + + placements M 1 M 2 + edges form a perfect matching in G contradiction. 19

Case 2: xz, yw are in same cycle of H. + y w + x + z + + + cements M 1 M 2 + edges form a perfect matching in G contradiction. Claim is proved. 20

Suppose G U has l odd components. Then l U from (3). l = U mod 2, since V is even. ------- Odd Components ---------- --Even Components--- U G has a perfect matching contradiction. 21

Petersen s Theorem Theorem 6 Every 3-regular graph without cutedges contains a perfect matching. Proof Suppose S V. Let G S have components C 1, C 2,..., C r where C 1, C 2,..., C l are odd. m i is the number of C i : S edges; m i 2. n i is the number of edges contained in C i. 3 C i = m i + 2n i. So m i is odd for 1 i l. Hence m i 3 for 1 i l. Thus 3l m 1 + m 2 + + m l 3 S, and (2) holds. 22