The Strong Perfect Graph Theorem

Total Page:16

File Type:pdf, Size:1020Kb

The Strong Perfect Graph Theorem Annals of Mathematics, 164 (2006), 51–229 The strong perfect graph theorem ∗ ∗ By Maria Chudnovsky, Neil Robertson, Paul Seymour, * ∗∗∗ and Robin Thomas Abstract A graph G is perfect if for every induced subgraph H, the chromatic number of H equals the size of the largest complete subgraph of H, and G is Berge if no induced subgraph of G is an odd cycle of length at least five or the complement of one. The “strong perfect graph conjecture” (Berge, 1961) asserts that a graph is perfect if and only if it is Berge. A stronger conjecture was made recently by Conforti, Cornu´ejols and Vuˇskovi´c — that every Berge graph either falls into one of a few basic classes, or admits one of a few kinds of separation (designed so that a minimum counterexample to Berge’s conjecture cannot have either of these properties). In this paper we prove both of these conjectures. 1. Introduction We begin with definitions of some of our terms which may be nonstandard. All graphs in this paper are finite and simple. The complement G of a graph G has the same vertex set as G, and distinct vertices u, v are adjacent in G just when they are not adjacent in G.Ahole of G is an induced subgraph of G which is a cycle of length at least 4. An antihole of G is an induced subgraph of G whose complement is a hole in G. A graph G is Berge if every hole and antihole of G has even length. A clique in G is a subset X of V (G) such that every two members of X are adjacent. A graph G is perfect if for every induced subgraph H of G, *Supported by ONR grant N00014-01-1-0608, NSF grant DMS-0071096, and AIM. ∗∗ Supported by ONR grants N00014-97-1-0512 and N00014-01-1-0608, and NSF grant DMS-0070912. ∗∗∗ Supported by ONR grant N00014-01-1-0608, NSF grants DMS-9970514 and DMS- 0200595, and AIM. 52 M. CHUDNOVSKY, N. ROBERTSON, P. SEYMOUR, AND R. THOMAS the chromatic number of H equals the size of the largest clique of H. The study of perfect graphs was initiated by Claude Berge, partly motivated by a problem from information theory (finding the “Shannon capacity” of a graph — it lies between the size of the largest clique and the chromatic number, and so for a perfect graph it equals both). In particular, in 1961 Berge [1] proposed two celebrated conjectures about perfect graphs. Since the second implies the first, they were known as the “weak” and “strong” perfect graph conjectures respectively, although both are now theorems: 1.1. The complement of every perfect graph is perfect. 1.2. A graph is perfect if and only if it is Berge. The first was proved by Lov´asz [16] in 1972. The second, the strong perfect graph conjecture, received a great deal of attention over the past 40 years, but remained open until now, and is the main theorem of this paper. Since every perfect graph is Berge, to prove 1.2 it remains to prove the converse. By a minimum imperfect graph we mean a counterexample to 1.2 with as few vertices as possible (in particular, any such graph is Berge and not perfect). Much of the published work on 1.2 falls into two classes: proving that the theorem holds for graphs with some particular graph excluded as an induced subgraph, and investigating the structure of minimum imperfect graphs. For the latter, linear programming methods have been particularly useful; there are rich connections between perfect graphs and linear and integer programming (see [5], [20] for example). But a third approach has been developing in the perfect graph community over a number of years; the attempt to show that every Berge graph either belongs to some well-understood basic class of (perfect) graphs, or admits some feature that a minimum imperfect graph cannot admit. Such a result would therefore prove that no minimum imperfect graph exists, and consequently prove 1.2. Our main result is of this type, and our first goal is to state it. Thus, let us be more precise: we start with two definitions. We say that G is a double split graph if V (G) can be partioned into four sets {a1,...,am}, {b1,...,bm}, {c1,...,cn}, {d1,...,dn} for some m, n ≥ 2, such that: • ai is adjacent to bi for 1 ≤ i ≤ m, and cj is nonadjacent to dj for 1 ≤ j ≤ n. • There are no edges between {ai,bi} and {ai ,bi } for 1 ≤ i<i ≤ m, and all four edges between {cj,dj} and {cj ,dj } for 1 ≤ j<j ≤ n. • There are exactly two edges between {ai,bi} and {cj,dj} for 1 ≤ i ≤ m and 1 ≤ j ≤ n, and these two edges have no common end. (A double split graph is so named because it can be obtained from what is called a “split graph” by doubling each vertex.) The line graph L(G) of a graph G has vertex set the set E(G) of edges of G, and e, f ∈ E(G) are adjacent in THE STRONG PERFECT GRAPH THEOREM 53 L(G) if they share an end in G. Let us say a graph G is basic if either G or G is bipartite or is the line graph of a bipartite graph, or is a double split graph. (Note that if G is a double split graph then so is G.) It is easy to see that all basic graphs are perfect. (For bipartite graphs this is trivial; for line graphs of bipartite graphs it is a theorem of K¨onig [15]; for their complements it follows from Lov´asz’ Theorem 1.1, although originally these were separate theorems of K¨onig; and for double split graphs we leave it to the reader.) Now we turn to the various kinds of “features” that we will prove exist in every Berge graph that is not basic. They are all decompositions of one kind or another, so henceforth we call them that. If X ⊆ V (G) we denote the subgraph of G induced on X by G|X. First, there is a special case of the “2-join” due to Cornu´ejols and Cunningham [13]: a proper 2-join in G is a partition (X1,X2)of V (G) such that there exist disjoint nonempty Ai,Bi ⊆ Xi (i =1, 2) satisfying: • Every vertex of A1 is adjacent to every vertex of A2, and every vertex of B1 is adjacent to every vertex of B2. • There are no other edges between X1 and X2. • For i =1, 2, every component of G|Xi meets both Ai and Bi, and • For i =1, 2, if |Ai| = |Bi| = 1 and G|Xi is a path joining the members of Ai and Bi, then it has odd length ≥ 3. (Thanks to Kristina Vuˇskovi´c for pointing out that we could include the “odd length” condition above with no change to the proof.) If X ⊆ V (G) and v ∈ V (G), we say v is X-complete if v is adjacent to every vertex in X (and consequently v/∈ X), and v is X-anticomplete if v has no neighbours in X.IfX, Y ⊆ V (G) are disjoint, we say X is complete to Y (or the pair (X, Y )iscomplete) if every vertex in X is Y -complete; and being anticomplete to Y is defined similarly. Our second decomposition is a slight variation of the “homogeneous pair” of Chv´atal and Sbihi [7] — a proper homogeneous pair in G is a pair of disjoint nonempty subsets (A, B)ofV (G), such that, if A1,A2 respectively denote the sets of all A-complete vertices and all A-anticomplete vertices in V (G), and B1,B2 are defined similarly, then: • A1 ∪ A2 = B1 ∪ B2 = V (G) \ (A ∪ B) (and in particular, every vertex in A has a neighbour in B and a nonneighbour in B, and vice versa). • The four sets A1 ∩ B1,A1 ∩ B2,A2 ∩ B1,A2 ∩ B2 are all nonempty. A path in G is an induced subgraph of G which is nonnull, connected, not a cycle, and in which every vertex has degree ≤ 2 (this definition is highly nonstandard, and we apologise, but it avoids writing “induced” about 600 times). An antipath is an induced subgraph whose complement is a path. The length of a path is the number of edges in it (and the length of an antipath is the number of edges in its complement). We therefore recognize paths and antipaths of length 0. If P is a path, P ∗ denotes the set of internal vertices 54 M. CHUDNOVSKY, N. ROBERTSON, P. SEYMOUR, AND R. THOMAS of P , called the interior of P ; and similarly for antipaths. Let A, B be disjoint subsets of V (G). We say the pair (A, B)isbalanced if there is no odd path between nonadjacent vertices in B with interior in A, and there is no odd antipath between adjacent vertices in A with interior in B. A set X ⊆ V (G) is connected if G|X is connected (so ∅ is connected); and anticonnected if G|X is connected. The third kind of decomposition used is due to Chv´atal [6] — a skew partition in G is a partition (A, B)ofV (G) such that A is not connected and B is not anticonnected. Despite their elegance, skew partitions pose a difficulty that the other two decompositions do not, for it has not been shown that a minimum imperfect graph cannot admit a skew partition; indeed, this is a well-known open question, first raised by Chv´atal [6], the so-called “skew partition conjecture”.
Recommended publications
  • Combinatorics: a First Encounter
    Combinatorics: a first encounter Darij Grinberg Thursday 10th January, 2019at 1:17am (unfinished draft!) Contents 1. Preface1 1.1. Acknowledgments . .4 2. What is combinatorics?4 2.1. Notations and conventions . .4 1. Preface These notes (which are work in progress and will remain so for the foreseeable fu- ture) are meant as an introduction to combinatorics – the mathematical discipline that studies finite sets (roughly speaking). When finished, they will cover topics such as binomial coefficients, the principles of enumeration, permutations, parti- tions and graphs. The emphasis falls on enumerative combinatorics, meaning the art of computing sizes of finite sets (“counting”), and graph theory. I have tried to keep the presentation as self-contained and elementary as possible. The reader is assumed to be familiar with some basics such as induction proofs, equivalence relations and summation signs, as well as have some experience with mathematical proofs. One of the best places to catch up on these basics and to gain said experience is the MIT lecture notes [LeLeMe16] (particularly their first five chapters). Two other resources to familiarize oneself with proofs are [Hammac15] and [Day16]. Generally, most good books about “reading and writing mathemat- ics” or “introductions to abstract mathematics” should convey these skills, although the extent to which they actually do so may differ. These notes are accompanying two classes on combinatorics (Math 4707 and 4990) I am giving at the University of Minneapolis in Fall 2017. Here is a (subjective and somewhat random) list of recommended texts on the kinds of combinatorics that will be considered in these notes: • Enumerative combinatorics (aka counting): 1 Notes on graph theory (Thursday 10th January, 2019, 1:17am) page 2 – The very basics of the subject can be found in [LeLeMe16, Chapters 14– 15].
    [Show full text]
  • Forbidden Subgraph Characterization of Quasi-Line Graphs Medha Dhurandhar [email protected]
    Forbidden Subgraph Characterization of Quasi-line Graphs Medha Dhurandhar [email protected] Abstract: Here in particular, we give a characterization of Quasi-line Graphs in terms of forbidden induced subgraphs. In general, we prove a necessary and sufficient condition for a graph to be a union of two cliques. 1. Introduction: A graph is a quasi-line graph if for every vertex v, the set of neighbours of v is expressible as the union of two cliques. Such graphs are more general than line graphs, but less general than claw-free graphs. In [2] Chudnovsky and Seymour gave a constructive characterization of quasi-line graphs. An alternative characterization of quasi-line graphs is given in [3] stating that a graph has a fuzzy reconstruction iff it is a quasi-line graph and also in [4] using the concept of sums of Hoffman graphs. Here we characterize quasi-line graphs in terms of the forbidden induced subgraphs like line graphs. We consider in this paper only finite, simple, connected, undirected graphs. The vertex set of G is denoted by V(G), the edge set by E(G), the maximum degree of vertices in G by Δ(G), the maximum clique size by (G) and the chromatic number by G). N(u) denotes the neighbourhood of u and N(u) = N(u) + u. For further notation please refer to Harary [3]. 2. Main Result: Before proving the main result we prove some lemmas, which will be used later. Lemma 1: If G is {3K1, C5}-free, then either 1) G ~ K|V(G)| or 2) If v, w V(G) are s.t.
    [Show full text]
  • On Treewidth and Graph Minors
    On Treewidth and Graph Minors Daniel John Harvey Submitted in total fulfilment of the requirements of the degree of Doctor of Philosophy February 2014 Department of Mathematics and Statistics The University of Melbourne Produced on archival quality paper ii Abstract Both treewidth and the Hadwiger number are key graph parameters in structural and al- gorithmic graph theory, especially in the theory of graph minors. For example, treewidth demarcates the two major cases of the Robertson and Seymour proof of Wagner's Con- jecture. Also, the Hadwiger number is the key measure of the structural complexity of a graph. In this thesis, we shall investigate these parameters on some interesting classes of graphs. The treewidth of a graph defines, in some sense, how \tree-like" the graph is. Treewidth is a key parameter in the algorithmic field of fixed-parameter tractability. In particular, on classes of bounded treewidth, certain NP-Hard problems can be solved in polynomial time. In structural graph theory, treewidth is of key interest due to its part in the stronger form of Robertson and Seymour's Graph Minor Structure Theorem. A key fact is that the treewidth of a graph is tied to the size of its largest grid minor. In fact, treewidth is tied to a large number of other graph structural parameters, which this thesis thoroughly investigates. In doing so, some of the tying functions between these results are improved. This thesis also determines exactly the treewidth of the line graph of a complete graph. This is a critical example in a recent paper of Marx, and improves on a recent result by Grohe and Marx.
    [Show full text]
  • Infinitely Many Minimal Classes of Graphs of Unbounded Clique-Width∗
    Infinitely many minimal classes of graphs of unbounded clique-width∗ A. Collins, J. Foniok†, N. Korpelainen, V. Lozin, V. Zamaraev Abstract The celebrated theorem of Robertson and Seymour states that in the family of minor-closed graph classes, there is a unique minimal class of graphs of unbounded tree-width, namely, the class of planar graphs. In the case of tree-width, the restriction to minor-closed classes is justified by the fact that the tree-width of a graph is never smaller than the tree-width of any of its minors. This, however, is not the case with respect to clique-width, as the clique-width of a graph can be (much) smaller than the clique-width of its minor. On the other hand, the clique-width of a graph is never smaller than the clique-width of any of its induced subgraphs, which allows us to be restricted to hereditary classes (that is, classes closed under taking induced subgraphs), when we study clique-width. Up to date, only finitely many minimal hereditary classes of graphs of unbounded clique-width have been discovered in the literature. In the present paper, we prove that the family of such classes is infinite. Moreover, we show that the same is true with respect to linear clique-width. Keywords: clique-width, linear clique-width, hereditary class 1 Introduction Clique-width is a graph parameter which is important in theoretical computer science, because many algorithmic problems that are generally NP-hard become polynomial-time solvable when restricted to graphs of bounded clique-width [4].
    [Show full text]
  • Counting Independent Sets in Graphs with Bounded Bipartite Pathwidth∗
    Counting independent sets in graphs with bounded bipartite pathwidth∗ Martin Dyery Catherine Greenhillz School of Computing School of Mathematics and Statistics University of Leeds UNSW Sydney, NSW 2052 Leeds LS2 9JT, UK Australia [email protected] [email protected] Haiko M¨uller∗ School of Computing University of Leeds Leeds LS2 9JT, UK [email protected] 7 August 2019 Abstract We show that a simple Markov chain, the Glauber dynamics, can efficiently sample independent sets almost uniformly at random in polynomial time for graphs in a certain class. The class is determined by boundedness of a new graph parameter called bipartite pathwidth. This result, which we prove for the more general hardcore distribution with fugacity λ, can be viewed as a strong generalisation of Jerrum and Sinclair's work on approximately counting matchings, that is, independent sets in line graphs. The class of graphs with bounded bipartite pathwidth includes claw-free graphs, which generalise line graphs. We consider two further generalisations of claw-free graphs and prove that these classes have bounded bipartite pathwidth. We also show how to extend all our results to polynomially-bounded vertex weights. 1 Introduction There is a well-known bijection between matchings of a graph G and independent sets in the line graph of G. We will show that we can approximate the number of independent sets ∗A preliminary version of this paper appeared as [19]. yResearch supported by EPSRC grant EP/S016562/1 \Sampling in hereditary classes". zResearch supported by Australian Research Council grant DP190100977. 1 in graphs for which all bipartite induced subgraphs are well structured, in a sense that we will define precisely.
    [Show full text]
  • General Approach to Line Graphs of Graphs 1
    DEMONSTRATIO MATHEMATICA Vol. XVII! No 2 1985 Antoni Marczyk, Zdzislaw Skupien GENERAL APPROACH TO LINE GRAPHS OF GRAPHS 1. Introduction A unified approach to the notion of a line graph of general graphs is adopted and proofs of theorems announced in [6] are presented. Those theorems characterize five different types of line graphs. Both Krausz-type and forbidden induced sub- graph characterizations are provided. So far other authors introduced and dealt with single spe- cial notions of a line graph of graphs possibly belonging to a special subclass of graphs. In particular, the notion of a simple line graph of a simple graph is implied by a paper of Whitney (1932). Since then it has been repeatedly introduc- ed, rediscovered and generalized by many authors, among them are Krausz (1943), Izbicki (1960$ a special line graph of a general graph), Sabidussi (1961) a simple line graph of a loop-free graph), Menon (1967} adjoint graph of a general graph) and Schwartz (1969; interchange graph which coincides with our line graph defined below). In this paper we follow another way, originated in our previous work [6]. Namely, we distinguish special subclasses of general graphs and consider five different types of line graphs each of which is defined in a natural way. Note that a similar approach to the notion of a line graph of hypergraphs can be adopted. We consider here the following line graphsi line graphs, loop-free line graphs, simple line graphs, as well as augmented line graphs and augmented loop-free line graphs. - 447 - 2 A. Marczyk, Z.
    [Show full text]
  • Lecture 10: April 20, 2005 Perfect Graphs
    Re-revised notes 4-22-2005 10pm CMSC 27400-1/37200-1 Combinatorics and Probability Spring 2005 Lecture 10: April 20, 2005 Instructor: L´aszl´oBabai Scribe: Raghav Kulkarni TA SCHEDULE: TA sessions are held in Ryerson-255, Monday, Tuesday and Thursday 5:30{6:30pm. INSTRUCTOR'S EMAIL: [email protected] TA's EMAIL: [email protected], [email protected] IMPORTANT: Take-home test Friday, April 29, due Monday, May 2, before class. Perfect Graphs k 1=k Shannon capacity of a graph G is: Θ(G) := limk (α(G )) : !1 Exercise 10.1 Show that α(G) χ(G): (G is the complement of G:) ≤ Exercise 10.2 Show that χ(G H) χ(G)χ(H): · ≤ Exercise 10.3 Show that Θ(G) χ(G): ≤ So, α(G) Θ(G) χ(G): ≤ ≤ Definition: G is perfect if for all induced sugraphs H of G, α(H) = χ(H); i. e., the chromatic number is equal to the clique number. Theorem 10.4 (Lov´asz) G is perfect iff G is perfect. (This was open under the name \weak perfect graph conjecture.") Corollary 10.5 If G is perfect then Θ(G) = α(G) = χ(G): Exercise 10.6 (a) Kn is perfect. (b) All bipartite graphs are perfect. Exercise 10.7 Prove: If G is bipartite then G is perfect. Do not use Lov´asz'sTheorem (Theorem 10.4). 1 Lecture 10: April 20, 2005 2 The smallest imperfect (not perfect) graph is C5 : α(C5) = 2; χ(C5) = 3: For k 2, C2k+1 imperfect.
    [Show full text]
  • Arxiv:2106.16130V1 [Math.CO] 30 Jun 2021 in the Special Case of Cyclohedra, and by Cardinal, Langerman and P´Erez-Lantero [5] in the Special Case of Tree Associahedra
    LAGOS 2021 Bounds on the Diameter of Graph Associahedra Jean Cardinal1;4 Universit´elibre de Bruxelles (ULB) Lionel Pournin2;4 Mario Valencia-Pabon3;4 LIPN, Universit´eSorbonne Paris Nord Abstract Graph associahedra are generalized permutohedra arising as special cases of nestohedra and hypergraphic polytopes. The graph associahedron of a graph G encodes the combinatorics of search trees on G, defined recursively by a root r together with search trees on each of the connected components of G − r. In particular, the skeleton of the graph associahedron is the rotation graph of those search trees. We investigate the diameter of graph associahedra as a function of some graph parameters. It is known that the diameter of the associahedra of paths of length n, the classical associahedra, is 2n − 6 for a large enough n. We give a tight bound of Θ(m) on the diameter of trivially perfect graph associahedra on m edges. We consider the maximum diameter of associahedra of graphs on n vertices and of given tree-depth, treewidth, or pathwidth, and give lower and upper bounds as a function of these parameters. Finally, we prove that the maximum diameter of associahedra of graphs of pathwidth two is Θ(n log n). Keywords: generalized permutohedra, graph associahedra, tree-depth, treewidth, pathwidth 1 Introduction The vertices and edges of a polyhedron form a graph whose diameter (often referred to as the diameter of the polyhedron for short) is related to a number of computational problems. For instance, the question of how large the diameter of a polyhedron arises naturally from the study of linear programming and the simplex algorithm (see, for instance [27] and references therein).
    [Show full text]
  • Vertex Deletion Problems on Chordal Graphs∗†
    Vertex Deletion Problems on Chordal Graphs∗† Yixin Cao1, Yuping Ke2, Yota Otachi3, and Jie You4 1 Department of Computing, Hong Kong Polytechnic University, Hong Kong, China [email protected] 2 Department of Computing, Hong Kong Polytechnic University, Hong Kong, China [email protected] 3 Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan [email protected] 4 School of Information Science and Engineering, Central South University and Department of Computing, Hong Kong Polytechnic University, Hong Kong, China [email protected] Abstract Containing many classic optimization problems, the family of vertex deletion problems has an important position in algorithm and complexity study. The celebrated result of Lewis and Yan- nakakis gives a complete dichotomy of their complexity. It however has nothing to say about the case when the input graph is also special. This paper initiates a systematic study of vertex deletion problems from one subclass of chordal graphs to another. We give polynomial-time algorithms or proofs of NP-completeness for most of the problems. In particular, we show that the vertex deletion problem from chordal graphs to interval graphs is NP-complete. 1998 ACM Subject Classification F.2.2 Analysis of Algorithms and Problem Complexity, G.2.2 Graph Theory Keywords and phrases vertex deletion problem, maximum subgraph, chordal graph, (unit) in- terval graph, split graph, hereditary property, NP-complete, polynomial-time algorithm Digital Object Identifier 10.4230/LIPIcs.FSTTCS.2017.22 1 Introduction Generally speaking, a vertex deletion problem asks to transform an input graph to a graph in a certain class by deleting a minimum number of vertices.
    [Show full text]
  • Algorithms for Deletion Problems on Split Graphs
    Algorithms for deletion problems on split graphs Dekel Tsur∗ Abstract In the Split to Block Vertex Deletion and Split to Threshold Vertex Deletion problems the input is a split graph G and an integer k, and the goal is to decide whether there is a set S of at most k vertices such that G − S is a block graph and G − S is a threshold graph, respectively. In this paper we give algorithms for these problems whose running times are O∗(2.076k) and O∗(2.733k), respectively. Keywords graph algorithms, parameterized complexity. 1 Introduction A graph G is called a split graph if its vertex set can be partitioned into two disjoint sets C and I such that C is a clique and I is an independent set. A graph G is a block graph if every biconnected component of G is a clique. A graph G is a threshold graph if there is a t ∈ R and a function f : V (G) → R such that for every u, v ∈ V (G), (u, v) is an edge in G if and only if f(u)+ f(v) ≥ t. In the Split to Block Vertex Deletion (SBVD) problem the input is a split graph G and an integer k, and the goal is to decide whether there is a set S of at most k vertices such that G − S is a block graph. Similarly, in the Split to Threshold Vertex Deletion (STVD) problem the input is a split graph G and an integer k, and the goal is to decide whether there is a set S of at most k vertices such that G − S is a threshold graph.
    [Show full text]
  • Decomposing Berge Graphs and Detecting Balanced Skew Partitions Nicolas Trotignon
    Decomposing Berge graphs and detecting balanced skew partitions Nicolas Trotignon To cite this version: Nicolas Trotignon. Decomposing Berge graphs and detecting balanced skew partitions. 2006. halshs- 00115625 HAL Id: halshs-00115625 https://halshs.archives-ouvertes.fr/halshs-00115625 Submitted on 22 Nov 2006 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Centre d’Economie de la Sorbonne UMR 8174 Decomposing Berge graphs and detecting balanced skew partitions Nicolas TROTIGNON 2006.36 Maison des Sciences Économiques, 106-112 boulevard de L'Hôpital, 75647 Paris Cedex 13 http://mse.univ-paris1.fr/Publicat.htm ISSN : 1624-0340 Decomposing Berge graphs and detecting balanced skew partitions Nicolas Trotignon∗ April 26, 2006 Abstract A hole in a graph is an induced cycle on at least four vertices. A graph is Berge if it has no odd hole and if its complement has no odd hole. In 2002, Chudnovsky, Robertson, Seymour and Thomas proved a decomposition theorem for Berge graphs saying that every Berge graph either is in a well understood basic class or has some kind of decomposition. Then, Chudnovsky proved stronger theorems. One of them restricts the allowed decompositions to 2-joins and balanced skew partitions.
    [Show full text]
  • Online Graph Coloring
    Online Graph Coloring Jinman Zhao - CSC2421 Online Graph coloring Input sequence: Output: Goal: Minimize k. k is the number of color used. Chromatic number: Smallest number of need for coloring. Denoted as . Lower bound Theorem: For every deterministic online algorithm there exists a logn-colorable graph for which the algorithm uses at least 2n/logn colors. The performance ratio of any deterministic online coloring algorithm is at least . Transparent online coloring game Adversary strategy : The collection of all subsets of {1,2,...,k} of size k/2. Avail(vt): Admissible colors consists of colors not used by its pre-neighbors. Hue(b)={Corlor(vi): Bin(vi) = b}: hue of a bin is the set of colors of vertices in the bin. H: hue collection is a set of all nonempty hues. #bin >= n/(k/2) #color<=k ratio>=2n/(k*k) Lower bound Theorem: For every randomized online algorithm there exists a k- colorable graph on which the algorithm uses at least n/k bins, where k=O(logn). The performance ratio of any randomized online coloring algorithm is at least . Adversary strategy for randomized algo Relaxing the constraint - blocked input Theorem: The performance ratio of any randomized algorithm, when the input is presented in blocks of size , is . Relaxing other constraints 1. Look-ahead and bufferring 2. Recoloring 3. Presorting vertices by degree 4. Disclosing the adversary’s previous coloring First Fit Use the smallest numbered color that does not violate the coloring requirement Induced subgraph A induced subgraph is a subset of the vertices of a graph G together with any edges whose endpoints are both in the subset.
    [Show full text]