DOCSLIB.ORG

Complétions D'intervalles Minimales

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Complétions D'intervalles Minimales Complétions d’intervalles minimales Karol Suchan To cite this version: Karol Suchan. Complétions d’intervalles minimales. Computer Science [cs]. Université d’Orléans, 2006. English. tel-00480669 HAL Id: tel-00480669 https://tel.archives-ouvertes.fr/tel-00480669 Submitted on 4 May 2010 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. UNIVERSITE D'ORLEANS THESE PRESENTEE A L’UNIVERSITE D’ORLEANS POUR OBTENIR LE GRADE DE DOCTEUR DE L’UNIVERSITE D’ORLEANS Discipline : Informatique PAR M. Karol SUCHAN Sujet : Complétions d'intervalles minimales Soutenue le : 12 décembre 2006 MEMBRES DU JURY : - Mme Anne BERRY (LIMOS, Clermont Ferrand) Examinateur - M. Krzysztof DIKS (Université de Varsovie) Rapporteur - M. Pierre FRAIGNIAUD (LRI, Université Paris XI) Rapporteur - M. Michel HABIB (LIAFA, Université Paris 7) Examinateur - M. Henri THUILLIER (LIFO, Université d’Orléans) Directeur de thèse - M. Ioan TODINCA (LIFO, Université d’Orléans) Encadrant Contents 1 Introduction 1 2 Basis 9 2.1 Basicnotions .................................... 9 2.1.1 Minimalseparators. .. .. .. .. .. .. .. .. 10 2.1.2 Orderedpartitions . .. .. .. .. .. .. .. .. 10 2.2 Intersectiongraphs .............................. 11 2.2.1 Intersectionmodel . .. .. .. .. .. .. .. .. 11 2.2.2 Connected decomposition . 13 2.2.3 Layouts ....................................... 14 2.2.4 Other characterizations and properties . ........... 15 2.3 Completion ...................................... 16 3 MPIC Through Decomposition 19 3.1 ProperIntervallayout. 20 3.2 MoplexandLexBFS ................................. 21 3.3 Nicelayoutsandniceprefixes . 22 3.3.1 Choosingafirstvertex. 22 3.3.2 Afamilyofnicelayouts . 23 3.3.3 Nice layouts : a sufficient condition . 26 3.4 Thealgorithm.................................... 27 3.5 Conclusions ..................................... 30 4 MIC Through Exploration 33 4.1 Intervallayout .................................. 33 4.2 Nicelayoutsandniceprefixes . 34 4.2.1 LexBFS-terminalvertex . 35 4.2.2 Choosingafirstvertex. 35 4.2.3 Afamilyofnicelayouts . 36 4.2.4 Nice layouts : a sufficient condition . 38 4.3 Thealgorithm.................................... 40 4.4 Conclusions ..................................... 41 iii 5 Pre-order - clique path encoding 43 5.1 Controlovercliquepaths . 44 5.2 Structureofthepre-order . 49 5.2.1 The co-comparability graph . 49 5.3 Learningthepre-order . 52 5.3.1 Topologicalorder.............................. 52 5.3.2 Bipartition ................................... 54 5.3.3 Components of (O(S), !) ............................. 55 5.3.4 Complexity .................................... 55 6 MIC Through Decomposition 57 6.1 Incrementalapproach ............................. 58 6.2 Principlesofthealgorithm. ......... 59 6.2.1 NG! (x)isaclique.................................. 60 6.2.2 NG! (x)isnotaclique ............................... 61 6.3 Minimalcompletion ............................... 63 6.3.1 Minimal separators in Sx ............................. 63 6.4 AlgorithmNicePair............................... 67 6.5 Puttingeverythingtogether . ......... 70 6.5.1 Algorithm MinimalIntervalCompletion . .......... 72 6.6 Conclusions ..................................... 73 7 Pathwidth of Circular-Arc Graphs 75 7.1 Folding ......................................... 75 7.2 Circular-arcgraphs.............................. 77 7.3 Foldingcircular-arcgraphs. .......... 78 7.4 Thealgorithm.................................... 83 7.5 Conclusions ..................................... 86 8 MIC Extraction 87 8.1 Foldingintervalgraphs. 87 8.2 Unfolding ....................................... 91 8.3 Extracting minimal interval completions: the algorithm ................ 96 8.4 ProofofLemma8.3.2 ............................... 98 8.5 Conclusions ..................................... 101 iv List of Figures 2.1 A proper interval graph and an interval model. ............ 12 2.2 A slight modification of intervals to avoid strict inclusion. ............... 12 3.1 PIG(G, σ)-thefilledgesindashedline-style. 20 3.2 The LexBFS algorithm.................................... 22 3.3 BFS color code and the strong neighborhood of ρ. ................... 23 3.4 The strong neighborhood of ρ shouldcomefirst. .................... 24 3.5 The weak neighborhood comes before the non-neighborhood. ............. 25 3.6 A vertex in NS(ρ)havingfewwhiteneighborsshouldcomefirst. 26 3.7 Algorithm Minimal Proper Interval Completion and data structure. 28 3.8 AlgorithmIntervalModel. 29 4.1 IG(G, σ)-dashedfilledges. ................................ 34 4.2 Putting Nxt right after ρ. ................................. 37 4.3 Algorithm MIC Ordering. ................................. 41 5.1 Blocks associated to a minimal separator. ............ 44 6.1 Pruning x from a clique path of G............................. 59 6.2 Two clique paths of G - different intervals [KL : KR]................... 60 6.3 Two ordered partitions of G - different intervals [OL : OR]................ 64 6.4 Main case: the algorithm constructing a nice pair (L, R). ............... 69 7.1 The FillFolding algorithm. ............................... 76 7.2 Circular-arcgraph. .............................. 77 7.3 Restriction of the clique cycle. ........... 78 7.4 FillFolding......................................... 79 7.5 Reduction of A (top) to A! : one way (middle) or the other (bottom). 80 7.6 From 4-monotone to 2-monotone foldings. ........... 81 7.7 Planar triangulation corresponding to a 2-folding. ................. 84 8.1 Unfolding....................................... 92 8.2 Openingonepivot. ................................ 96 8.3 Openingtwopivots. ............................... 97 8.4 Extracting a minimal interval completion. ............. 98 8.5 2-folding. ...................................... 99 v vi Chapter 1 Introduction Research on NP-hard problems is in the core of contemporary algorithmics. Since it is impractical to search for general exact solutions to the problems in this class, many special approaches have been developed. Most efforts can be regrouped about the following main axis: heuristics, guaranteed quality approximations, exact solutions for restrained graph classes, and the most recently, general exact exponential time algorithms. Heuristics are often fast and prove very useful in applications but admit no theoretical proofs of quality. They are evaluated in empirical manner with benchmarks running on real-life data coming from applications or on random data-sets generated from adequate probability distributions. Approximation algorithms are polynomial time algorithms that give solutions that fit within bounds proved to be close to optimum. How tight these bounds can be depends on the problem. The most common are the approximations within a constant multiplicative factor, meaning that their accuracy is independent from the instance. But sometimes multiplicative constant factor polynomial time approximations are proved not to exist (again, unless P=NP). Graph coloring is an important example of such a problem. The third approach to NP-hard problems consists in finding polynomial time exact algorithms for restrained graph classes. Here the question is how strong constraints on the structure of the graph in question we need to have in order to be able to solve the problem in polynomial time. It is the usual path to start with strong conditions sufficient to prove polynomial time complexity, that are progressively relaxed as the understanding of the problem improves. Thus the class of graphs for which the problem can be efficiently solved becomes larger and larger. Eventually, the study may accomplish by finding conditions that are also necessary, hence a full characterization of instances solvable in polynomial time is given. Finally, a new branch of exponential time algorithms has been recently developed. Their interest is twofold. First, they aim at finding exponential time algorithms with small basis of the exponent, which can make them of practical use for instances of moderate size if the basis is close enough to 1. Then, the complexity analysis may serve to evaluate on how adequate are the tools used to describe the combinatorial structure of the problem, which stimulates further research. It is worth mentioning here that these approaches to NP-hard problems are in no way exclusive. They rather tend to complement and reinforce each other: observations on exact solutions for particular cases are incorporated into heuristics, heuristics are refined into approximation algorithms, approximations prove to be exact for particular instances, etc. One of the methods used for tackling NP-hard problems on restrained graph classes is through graph decompositions. The basic idea is to decompose the vertex set or the edge set of a graph into clusters which satisfy certain conditions. The properties of this decomposition are such that 1 if one can solve the problem on the clusters, then these partial solutions can be joined to yield a global solution. Typically, the clusters are decomposed recursively, which gives the structure of
Load more

Recommended publications
  • Interval Completion with Few Edges∗
    Interval Completion with Few Edges∗ Pinar Heggernes Christophe Paul Department of Informatics CNRS - LIRMM, and University of Bergen School of Computer Science Norway McGill Univ. Canada [email protected] [email protected] Jan Arne Telle Yngve Villanger Department of Informatics Department of Informatics University of Bergen University of Bergen Norway Norway [email protected] [email protected] ABSTRACT Keywords We present an algorithm with runtime O(k2kn3m) for the Interval graphs, physical mapping, profile minimization, following NP-complete problem [8, problem GT35]: Given edge completion, FPT algorithm, branching an arbitrary graph G on n vertices and m edges, can we obtain an interval graph by adding at most k new edges to G? This resolves the long-standing open question [17, 6, 24, 1. INTRODUCTION AND MOTIVATION 13], first posed by Kaplan, Shamir and Tarjan, of whether Interval graphs are the intersection graphs of intervals of this problem could be solved in time f(k) nO(1). The prob- the real line and have a wide range of applications [12]. lem has applications in Physical Mapping· of DNA [11] and Connected with interval graphs is the following problem: in Profile Minimization for Sparse Matrix Computations [9, Given an arbitrary graph G, what is the minimum number 25]. For the first application, our results show tractability of edges that must be added to G in order to obtain an in- for the case of a small number k of false negative errors, and terval graph? This problem is NP-hard [18, 8] and it arises for the second, a small number k of zero elements in the in both Physical Mapping of DNA and Sparse Matrix Com- envelope.
    [Show full text]
  • A Subexponential Parameterized Algorithm for Proper Interval Completion
    A subexponential parameterized algorithm for Proper Interval Completion Ivan Bliznets1 Fedor V. Fomin2 Marcin Pilipczuk2 Micha l Pilipczuk2 1St. Petersburg Academic University of the Russian Academy of Sciences, Russia 2Department of Informatics, University of Bergen, Norway ESA'14, Wroc law, September 9th, 2014 Bliznets, Fomin, Pilipczuk×2 SubExp for PIC 1/17 Proper interval graphs: graphs admitting an intersection model of intervals on a line s.t. no interval contains any other interval. Unit interval graphs: graphs admitting an intersection model of unit intervals on a line. PIG = UIG. (Proper) interval graphs Interval graphs: graphs admitting an intersection model of intervals on a line. Bliznets, Fomin, Pilipczuk×2 SubExp for PIC 2/17 Unit interval graphs: graphs admitting an intersection model of unit intervals on a line. PIG = UIG. (Proper) interval graphs Interval graphs: graphs admitting an intersection model of intervals on a line. Proper interval graphs: graphs admitting an intersection model of intervals on a line s.t. no interval contains any other interval. Bliznets, Fomin, Pilipczuk×2 SubExp for PIC 2/17 PIG = UIG. (Proper) interval graphs Interval graphs: graphs admitting an intersection model of intervals on a line. Proper interval graphs: graphs admitting an intersection model of intervals on a line s.t. no interval contains any other interval. Unit interval graphs: graphs admitting an intersection model of unit intervals on a line. Bliznets, Fomin, Pilipczuk×2 SubExp for PIC 2/17 (Proper) interval graphs Interval graphs: graphs admitting an intersection model of intervals on a line. Proper interval graphs: graphs admitting an intersection model of intervals on a line s.t.
    [Show full text]
  • Parallel Gaussian Elimination with Linear Fill
    Parallelizing Elimination Orders with Linear Fill Claudson Bornstein Bruce Maggs Gary Miller R Ravi ;; School of Computer Science and Graduate School of Industrial Administration Carnegie Mellon University Pittsburgh PA Abstract pivoting step variable x is eliminated from equa i This paper presents an algorithm for nding parallel tions i i n elimination orders for Gaussian elimination Viewing The system of equations is typically represented as a system of equations as a graph the algorithm can be a matrix and as the pivots are p erformed some entries applied directly to interval graphs and chordal graphs in the matrix that were originally zero may b ecome For general graphs the algorithm can be used to paral nonzero The number of new nonzeros pro duced in lelize the order produced by some other heuristic such solving the system is called the l l Among the many as minimum degree In this case the algorithm is ap dierent orders of the variables one is typically chosen plied to the chordal completion that the heuristic gen so as to minimize the ll Minimizing the ll is desir erates from the input graph In general the input to able b ecause it limits the amount of storage needed to the algorithm is a chordal graph G with n nodes and solve the problem and also b ecause the ll is strongly m edges The algorithm produces an order with height correlated with the total number of op erations work p erformed at most O log n times optimal l l at most O m and work at most O W G where W G is the Gaussian elimination can also b e viewed
    [Show full text]
  • The Wonderful World of Chordal Graphs
    The Wonderful World of Chordal Graphs Martin Charles Golumbic University of Haifa, Israel The 32nd European Conference on Combinatorial Optimization, ECCO XXXII, Malta, 2019 Preamble In 1970, Claude Berge published the original French version his fundamental and perhaps most important book, Graphes et Hypergraphes. To many graph theorists, its chapters were saplings ready to be cultivated into the vast forest that we know today. As we enter this 50th (Jubilee) anniversary year, we celebrate Le Bois de Berge with its mathematical palms, pines and poplars, firs, fruit and ficuses, oaks, maples and cacti. One of those sapling chapters was on Perfect Graphs. Berge challenged us with his Perfect Graph Conjecture, and surveyed its core subclasses: comparability graphs, interval graphs, and triangulated (chordal) graphs. —citing Fulkerson, Gallai, Ghouila-Houri, Gilmore, Hoffman, Hojós, Lovász. By the time the English version appeared in 1973, more sprouts could have been added to the blossoming family —Benzer, Dirac, Fishburn, Gavril, Roberts, Rose, Trotter. However, these and others would wait until 1980, when my own book Algorithmic Graph Theory and Perfect Graphs first appeared. —a direct outgrowth of Berge’s inspiring chapter. Triangulated graphs – also known as rigid circuit graphs (Dirac), acyclic graphs (Lekkerkerker and Boland) But it was Fanica Gavril who coined the term chordal graphs. A graph G is a chordal graph, if every cycle in G of length greater than or equal to 4 has a chord, that is, an edge connecting two vertices that are not consecutive on the cycle. NOT a chordal graph This IS a It has many copies of C4 chordal graph Fanica Gavril: “I knew that these graphs occurred before as triangulated graphs, but the term triangulated was also used for maximal planar graphs, implying the statement, ‘some planar triangulated graphs are not triangulated graphs’ (like the complete wheels).
    [Show full text]
  • An O(N 2) Algorithm for the Minimal Interval Completion Problem
    An O(n2) algorithm for the minimal interval completion problem Christophe Crespelle1 and Ioan Todinca2 1 LIP6, Universite´ Paris 6 [email protected] 2 LIFO, Universite´ d’Orleans, BP 6759, F-45067 Orleans Cedex 2, France [email protected] Abstract. The minimal interval completion problem consists in adding edges to an arbitrary graph so that the resulting graph is an interval graph; the objective is to add an inclusion minimal set of edges, which means that no proper subset of the added edges can result in an interval graph when added to the original graph. We give an O(n2) time algorithm to obtain a minimal interval completion of an arbitrary graph. 1 Introduction Arbitrary graphs are complex combinatorial objects, thus it is very common to tempt to ”simplify” them using few transformations. Classically, we want to know if an in- put graph can be modified into a graph with a special property (e. g. into a planar or chordal graph) using only a few operations like edge and/or vertex additions or dele- tions. Several important graph parameters like treewidth, pathwidth or bandwidth can be defined in terms of graph completion problems. In graph completions we are only allowed to add edges to the input graph G = (V; E), in order to obtain a new graph H = (V; E [ F ) with the required property. For example the treewidth problem con- sists in computing a chordal completion H – also known as triangulation – of the input graph G, such that the maximum cliquesize of H is as small as possible.
    [Show full text]
  • Interval Deletion Is Fixed-Parameter Tractable∗
    Interval Deletion is Fixed-Parameter Tractable∗ Yixin Caoy Daniel´ Marxy Abstract in F; interval graphs and chordal graphs, among oth- We study the minimum interval deletion problem, which ers, are hereditary. For such graph classes, the vertex asks for the removal of a set of at most k vertices to make deletion version can be considered as the most robust a graph on n vertices into an interval graph. We present variant, which in some sense encompasses both edge ad- a parameterized algorithm of runtime 10k · nO(1) for this dition and edge deletion: if G can be made a member problem, thereby showing its fixed-parameter tractability. of F by k1 edge additions and k2 edge deletions, then it can be also made a member of F by deleting at most 1 Introduction k1 + k2 vertices (e.g., by deleting one endvertex of each added/deleted edge). A graph is an interval graph if its vertices can be as- Unfortunately, most of these graph modification signed to intervals of the real line such that there is problems are computationally hard: for example, a an edge between two vertices if and only if their cor- classical result of Lewis and Yannakakis [23] shows responding intervals intersect. Interval graphs are the that the vertex deletion problem is NP-hard for every natural models for DNA chains in biology and many nontrivial and hereditary class F, and according to other applications, among which the most cited ones Lund and Yannakakis [24], they are also MAX SNP- include jobs scheduling in industrial engineering [2] and hard.
    [Show full text]
  • Chordal Deletion Is Fixed-Parameter Tractable
    Chordal deletion is fixed-parameter tractable Daniel´ Marx Humboldt-Universitat¨ zu Berlin [email protected] June 22, 2006 WG 2006 Bergen, Norway Chordal deletion is fixed-parameter tractable – p.1/17 Graph modification problems Problems of the following form: Given a graph G and an integer k, is it possible to add/delete k edges/vertices such that the result belongs to class G? Make the graph bipartite by deleting k vertices. Make the graph chordal by adding k edges. Make the graph an empty graph by deleting k vertices (VERTEX COVER). ... Chordal deletion is fixed-parameter tractable – p.2/17 Notation for graph classes A notation introduced by Cai [2003]: Definition: If G is a class of graphs, then we define the following classes of graphs: G + ke: a graph from G with k extra edges. G− ke: a graph from G with k edges deleted. G + kv: graphs that can be made to be in G by deleting k vertices. G− kv: a graph from G with k vertices deleted. Chordal deletion is fixed-parameter tractable – p.3/17 Notation for graph classes A notation introduced by Cai [2003]: Definition: If G is a class of graphs, then we define the following classes of graphs: G + ke: a graph from G with k extra edges. G− ke: a graph from G with k edges deleted. G + kv: graphs that can be made to be in G by deleting k vertices. G− kv: a graph from G with k vertices deleted. Theorem: [Lewis and Yannakakis, 1980] If G is a nontrivial hereditary graph property, then it is NP-hard to decide if a graph is in G + kv (k is part of the input).
    [Show full text]
  • Arxiv:1903.02904V1 [Cs.DS] 7 Mar 2019
    Halin graphs are 3-vertex-colorable except even wheels A. Kapanowski∗ and A. Krawczyk Institute of Physics, Jagiellonian University, ulica Łojasiewicza 11, 30-348 Kraków, Poland (Dated: March 8, 2019) Abstract A Halin graph is a graph obtained by embedding a tree having no nodes of degree two in the plane, and then adding a cycle to join the leaves of the tree in such a way that the resulting graph is planar. According to the four color theorem, Halin graphs are 4-vertex-colorable. On the other hand, they are not 2-vertex-colorable because they have triangles. We show that all Halin graphs are 3-vertex-colorable except even wheels. We also show how to find the perfect elimination ordering of a chordal completion for a given Halin graph. The algorithms are implemented in Python using the graphtheory package. Generators of random Halin graphs (general or cubic) are included. The source code is available from the public GitHub repository. arXiv:1903.02904v1 [cs.DS] 7 Mar 2019 ∗ Corresponding author: [email protected] 1 I. INTRODUCTION A Halin graph H = T [ C is a simple graph obtained by embedding a tree T , which has no nodes of degree two, in the plane. Then new edges connecting leaves and forming a cycle C are added in such a way that the resulting graph H is planar. These graphs were studied by Halin [1] and Kirkman (cubic graphs) [2]. Every Halin graph is Hamiltonian, 3-connected, and polyhedral. The simplest Halin graphs with n vertices and m edges are wheels Wn, then T is a star and C is formed from n − 1 vertices.
    [Show full text]
  • Exploiting Chordal Structure in Systems of Polynomial Equations
    Exploiting Chordal Structure in Systems of Polynomial Equations by Diego Fernando Cifuentes Pardo Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 2014 ○c Massachusetts Institute of Technology 2014. All rights reserved. Author.................................................................. Department of Electrical Engineering and Computer Science August 29, 2014 Certified by . Pablo Parrilo Professor Thesis Supervisor Accepted by............................................................. Leslie A. Kolodziejski Chairman, Department Committee on Graduate Theses 2 Exploiting Chordal Structure in Systems of Polynomial Equations by Diego Fernando Cifuentes Pardo Submitted to the Department of Electrical Engineering and Computer Science on August 29, 2014, in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering and Computer Science Abstract Chordal structure and bounded treewidth allow for efficient computation in linear al- gebra, graphical models, constraint satisfaction and many other areas. Nevertheless, it has not been studied whether chordality might also help solve systems of polynomials. We propose a new technique, which we refer to as chordal elimination, that relies in elimination theory and Gröbner bases. Chordal elimination can be seen as a general- ization of sparse linear algebra. Unlike the linear case, the elimination process may not be exact. Nonetheless, we show that our methods are well-behaved for a large family of problems. We also use chordal elimination to obtain a good sparse description of a structured system of polynomials. By mantaining the graph structure in all computa- tions, chordal elimination can outperform standard Gröbner basis algorithms in many cases.
    [Show full text]
  • A Cubic Vertex-Kernel for Trivially Perfect Editing
    A cubic vertex-kernel for Trivially Perfect Editing Ma¨elDumas1, Anthony Perez1, and Ioan Todinca1 1Univ. Orl´eans,INSA Centre Val de Loire, LIFO EA 4022, F-45067 Orl´eans,France May 19, 2021 Abstract We consider the Trivially Perfect Editing problem, where one is given an undirected graph G = (V; E) and a parameter k 2 N and seeks to edit (add or delete) at most k edges from G to obtain a trivially perfect graph. The related Trivially Perfect Completion and Trivially Perfect Deletion problems are obtained by only allowing edge additions or edge deletions, respectively. Trivially perfect graphs are both chordal and cographs, and have applications related to the tree- depth width parameter and to social network analysis. All variants of the problem are known to be NP-Complete [6, 28] and to admit so-called polynomial kernels [13, 22]. More precisely, the existence of an O(k3) vertex-kernel for Trivially Perfect Completion was announced by Guo [22] but without a stand-alone proof. More recently, Drange and Pilipczuk [13] provided O(k7) vertex- kernels for these problems and left open the existence of cubic vertex-kernels. In this work, we answer positively to this question for all three variants of the problem. Introduction A broad range of optimization problems on graphs are particular cases of so-called modification problems. Given an arbitrary graph G = (V; E) and an integer k, the question is whether G can be turned into a graph satisfying some desired property by at most k modifications. By modifications we mean, according to the problem, vertex deletions (as for Vertex Cover and Feedback Vertex Set where we aim to obtain graphs with no edges, or without cycles respectively) or edge deletions and/or additions (as for Minimum Fill-In, also known as Chordal Completion, where the goal is to obtain a chordal graph, with no induced cycles with four or more vertices, by adding at most k edges).
    [Show full text]
  • Chordal Completions of Planar Graphs
    Reprinted from JOURNAL OF COMBINATORIAL THEORY, Series B Vol. 62, No. I, September 1994 All Rights Reserved by Academic Press, New York and London Printed in Belgium Chordal Completions of Planar Graphs F. R. K. CHUNG Bell Communications Research, 445 South Street, Morristown, New Jersey 07960 AND DAVID MUMFORD Department of Mathematics, Harvard University, Cambridge, Massachusetts 02138 Received March 2, 1993 We prove that every planar graph on n vertices is contained in a chordal graph with at most cn log n edges for some abolsute constant c and this is best possible to within a constant factor. © 1994 Academic Press, Inc. 1. INTRODUCTION A graph is said to be chordal if every cycle with at least four vertices always contains a chord. (A chordal graph is sometimes called a triangulated graph in the literature. For graph-theoretical terminology, the reader is referred to [4].) A chordal completion of a graph G is a chordal graph with the same vertex set as G which contains all edges of G. The problem of interest is to find for a given graph a chordal completion with as few edges as possible. Also, it is desirable to have a chordal completion with small maximum degree. These problems are motivated by applications in computer vision and artificial intelligence (for further discussion see Section 6). We will prove the following. THEOREM 1. Every planar graph on n vertices has a chordal completion with cn log n edges for some absolute constant c. We remark that there is an 0(n log n) algorithm for constructing a chordal completion of a planar graph.
    [Show full text]
  • Stacked Treewidth and the Colin De Verdière Number
    Bachelorthesis Stacked Treewidth and the Colin de Verdière Number Lasse Wulf September 30th, 2016 Reviewers: Prof. Dr. Dorothea Wagner Prof. Maria Axenovich, Ph.D. Advisors: Dr. Ignaz Rutter Dr. Torsten Ueckerdt At the Department of Informatics Institute of Theoretical Computer Science Karlsruhe Institute of Technology Statement of Authorship I hereby declare that this document has been composed by myself and describes my own work, unless otherwise acknowledged in the text. Karlsruhe, September 30, 2016 iii Abstract: For k N, a k-tree is constructed by starting with a (k+1)-clique and then repeatedly connecting2 a new vertex to all vertices of an existing k-clique. A k-tree is called a stacked k-tree, if during its construction no two vertices are stacked onto the same k-clique. We introduce the stacked treewidth stw(G) of a graph G as the smallest integer k such that G is subgraph of a stacked k-tree. The Colin de Verdière number µ is a graph parameter introduced 1993 by Yves Colin de Verdière which has the interesting property that the outerplanar, planar and linklessly embeddable graphs are characterized by µ(G) 2, µ(G) 3, and µ(G) 4, respectively. Recent results by Fallat and Mitchell show≤ that for all≤ chordal graphs≤G, stw(G) = tw(G) + 1 if and only if µ(G) = tw(G) + 1. We show that the same relation holds for all planar graphs, but for each k 4, we find a counterexample H, such that stw(H) = tw(H)+1 = k+1, but µ(H) k.≥ Along the way, we develop new tools for handling stw( ) and we prove that stw( )≤is minor-monotone and behaves identical to µ( ) on clique-sums.· · · v Contents 1 Introduction 1 1.1 Overview .
    [Show full text]