Lower Bounds for Boxicity
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A Special Planar Satisfiability Problem and a Consequence of Its NP-Completeness
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector DISCRETE APPLIED MATHEMATICS ELSEVIER Discrete Applied Mathematics 52 (1994) 233-252 A special planar satisfiability problem and a consequence of its NP-completeness Jan Kratochvil Charles University, Prague. Czech Republic Received 18 April 1989; revised 13 October 1992 Abstract We introduce a weaker but still NP-complete satisfiability problem to prove NP-complete- ness of recognizing several classes of intersection graphs of geometric objects in the plane, including grid intersection graphs and graphs of boxicity two. 1. Introduction Intersection graphs of different types of geometric objects in the plane gained more attention in recent years, mainly in connection with fast development of computa- tional geometry and computer science. Just to mention the most frequently cited classes, these are interval graphs, circular arc graphs, circle graphs, permutation graphs, etc. If we consider only connected objects (more precisely arc-connected sets) the most general class of intersection graphs are string graphs (intersection graphs of curves in the plane) which were originally introduced by Sinden [16] in the connection with thin film RC-circuits. String graphs were then considered by several authors [4,6,7]. In a recent paper [S], I have shown that recognition of string graphs is NP-hard and in fact, the method developed in [S] is refined in this note to obtain other NP- completeness results. It is striking that so far no finite algorithm for string graph recognition is known. It seems that relatively simpler classes will arise if we consider straight-line segments instead of curves and furthermore, if these segments are allowed to follow only a bounded number of directions. -
Structural Parameterizations of Clique Coloring
Structural Parameterizations of Clique Coloring Lars Jaffke University of Bergen, Norway lars.jaff[email protected] Paloma T. Lima University of Bergen, Norway [email protected] Geevarghese Philip Chennai Mathematical Institute, India UMI ReLaX, Chennai, India [email protected] Abstract A clique coloring of a graph is an assignment of colors to its vertices such that no maximal clique is monochromatic. We initiate the study of structural parameterizations of the Clique Coloring problem which asks whether a given graph has a clique coloring with q colors. For fixed q ≥ 2, we give an O?(qtw)-time algorithm when the input graph is given together with one of its tree decompositions of width tw. We complement this result with a matching lower bound under the Strong Exponential Time Hypothesis. We furthermore show that (when the number of colors is unbounded) Clique Coloring is XP parameterized by clique-width. 2012 ACM Subject Classification Mathematics of computing → Graph coloring Keywords and phrases clique coloring, treewidth, clique-width, structural parameterization, Strong Exponential Time Hypothesis Digital Object Identifier 10.4230/LIPIcs.MFCS.2020.49 Related Version A full version of this paper is available at https://arxiv.org/abs/2005.04733. Funding Lars Jaffke: Supported by the Trond Mohn Foundation (TMS). Acknowledgements The work was partially done while L. J. and P. T. L. were visiting Chennai Mathematical Institute. 1 Introduction Vertex coloring problems are central in algorithmic graph theory, and appear in many variants. One of these is Clique Coloring, which given a graph G and an integer k asks whether G has a clique coloring with k colors, i.e. -
A New Spectral Bound on the Clique Number of Graphs
A New Spectral Bound on the Clique Number of Graphs Samuel Rota Bul`o and Marcello Pelillo Dipartimento di Informatica - University of Venice - Italy {srotabul,pelillo}@dsi.unive.it Abstract. Many computer vision and patter recognition problems are intimately related to the maximum clique problem. Due to the intractabil- ity of this problem, besides the development of heuristics, a research di- rection consists in trying to find good bounds on the clique number of graphs. This paper introduces a new spectral upper bound on the clique number of graphs, which is obtained by exploiting an invariance of a continuous characterization of the clique number of graphs introduced by Motzkin and Straus. Experimental results on random graphs show the superiority of our bounds over the standard literature. 1 Introduction Many problems in computer vision and pattern recognition can be formulated in terms of finding a completely connected subgraph (i.e. a clique) of a given graph, having largest cardinality. This is called the maximum clique problem (MCP). One popular approach to object recognition, for example, involves matching an input scene against a stored model, each being abstracted in terms of a relational structure [1,2,3,4], and this problem, in turn, can be conveniently transformed into the equivalent problem of finding a maximum clique of the corresponding association graph. This idea was pioneered by Ambler et. al. [5] and was later developed by Bolles and Cain [6] as part of their local-feature-focus method. Now, it has become a standard technique in computer vision, and has been employing in such diverse applications as stereo correspondence [7], point pattern matching [8], image sequence analysis [9]. -
Clique-Width and Tree-Width of Sparse Graphs
Clique-width and tree-width of sparse graphs Bruno Courcelle Labri, CNRS and Bordeaux University∗ 33405 Talence, France email: [email protected] June 10, 2015 Abstract Tree-width and clique-width are two important graph complexity mea- sures that serve as parameters in many fixed-parameter tractable (FPT) algorithms. The same classes of sparse graphs, in particular of planar graphs and of graphs of bounded degree have bounded tree-width and bounded clique-width. We prove that, for sparse graphs, clique-width is polynomially bounded in terms of tree-width. For planar and incidence graphs, we establish linear bounds. Our motivation is the construction of FPT algorithms based on automata that take as input the algebraic terms denoting graphs of bounded clique-width. These algorithms can check properties of graphs of bounded tree-width expressed by monadic second- order formulas written with edge set quantifications. We give an algorithm that transforms tree-decompositions into clique-width terms that match the proved upper-bounds. keywords: tree-width; clique-width; graph de- composition; sparse graph; incidence graph Introduction Tree-width and clique-width are important graph complexity measures that oc- cur as parameters in many fixed-parameter tractable (FPT) algorithms [7, 9, 11, 12, 14]. They are also important for the theory of graph structuring and for the description of graph classes by forbidden subgraphs, minors and vertex-minors. Both notions are based on certain hierarchical graph decompositions, and the associated FPT algorithms use dynamic programming on these decompositions. To be usable, they need input graphs of "small" tree-width or clique-width, that furthermore are given with the relevant decompositions. -
Graphs with Small Intersection Dimension Patrick Lillis Advisor: Dr
Graphs With Small Intersection Dimension Patrick Lillis Advisor: Dr. R. Sritharan Abstract An X-Graph Split Graphs The boxicity of a graph G, denoted as box(G), We prove that the split graph of a is defined as the minimum integer k such that G is an intersection graph of axis-parallel k- convex graph has boxicity at most 2, dimensional boxes. We examine some known using intersecting chain graphs. A properties of graphs with respect to boxicity, chain graph is always an interval graph, so a 2 chain graph as well as show boxicity results pertaining to Add edge to get B several classes of graphs, including split representation is equivalent to a 2- graphs, X-graphs, and powers of trees. We dimensional box representation. also propose efficient algorithms to produce We then prove than any X-Graph is the intersection of 2 convex graphs, A the relevant k-dimensional representations. Add edge to get A and B (see left). As any convex graph Introduction has boxicity at most 2, any X-graph The graph classes we study all have low then has boxicity at most 4. bounds on boxicity (e.g. a tree has boxicity at most 2), or some result pertaining to small Powers of Trees (left) A tree T, with Δ ≤ 3 boxicity (e.g. it is NP-complete to determine if We find a constant bound on the boxicity (below) An embedding of a split graph has boxicity at most 3). We of powers of trees with Δ at most 3; any T in a revised perfect study specific subclasses of these graph even power of such a tree has binary tree T’. -
Parameterized Algorithms for Recognizing Monopolar and 2
Parameterized Algorithms for Recognizing Monopolar and 2-Subcolorable Graphs∗ Iyad Kanj1, Christian Komusiewicz2, Manuel Sorge3, and Erik Jan van Leeuwen4 1School of Computing, DePaul University, Chicago, USA, [email protected] 2Institut für Informatik, Friedrich-Schiller-Universität Jena, Germany, [email protected] 3Institut für Softwaretechnik und Theoretische Informatik, TU Berlin, Germany, [email protected] 4Max-Planck-Institut für Informatik, Saarland Informatics Campus, Saarbrücken, Germany, [email protected] October 16, 2018 Abstract A graph G is a (ΠA, ΠB)-graph if V (G) can be bipartitioned into A and B such that G[A] satisfies property ΠA and G[B] satisfies property ΠB. The (ΠA, ΠB)-Recognition problem is to recognize whether a given graph is a (ΠA, ΠB )-graph. There are many (ΠA, ΠB)-Recognition problems, in- cluding the recognition problems for bipartite, split, and unipolar graphs. We present efficient algorithms for many cases of (ΠA, ΠB)-Recognition based on a technique which we dub inductive recognition. In particular, we give fixed-parameter algorithms for two NP-hard (ΠA, ΠB)-Recognition problems, Monopolar Recognition and 2-Subcoloring, parameter- ized by the number of maximal cliques in G[A]. We complement our algo- rithmic results with several hardness results for (ΠA, ΠB )-Recognition. arXiv:1702.04322v2 [cs.CC] 4 Jan 2018 1 Introduction A (ΠA, ΠB)-graph, for graph properties ΠA, ΠB , is a graph G = (V, E) for which V admits a partition into two sets A, B such that G[A] satisfies ΠA and G[B] satisfies ΠB. There is an abundance of (ΠA, ΠB )-graph classes, and important ones include bipartite graphs (which admit a partition into two independent ∗A preliminary version of this paper appeared in SWAT 2016, volume 53 of LIPIcs, pages 14:1–14:14. -
Geometric Representations of Graphs
' $ Geometric Representations of Graphs L. Sunil Chandran Assistant Professor Comp. Science and Automation Indian Institute of Science Bangalore- 560012. Email: [email protected] & 1 % ' $ • Conventionally graphs are represented as adjacency matrices, or adjacency lists. Algorithms are designed with such representations in mind usually. • It is better to look at the structure of graphs and find some representations that are suitable for designing algorithms- say for a class of problems. • Intersection graphs: The vertices correspond to the subsets of a set U. The vertices are made adjacent if and only if the corresponding subsets intersect. • We propose to use some nice geometric objects as the subsets- like spheres, cubes, boxes etc. Here U will be the set of points in a low dimensional space. & 2 % ' $ • There are many situations where an intersection graph of geometric objects arises naturally.... • Some times otherwise NP-hard algorithmic problems become polytime solvable if we have geometric representation of the graph in a space of low dimension. & 3 % ' $ Boxicity and Cubicity • Cubicity: Minimum dimension k such that G can be represented as the intersection graph of k-dimensional cubes. • Boxicity: Minimum dimension k such that G can be represented as the intersection graph of k-dimensional axis parallel boxes. • These concepts were introduced by F. S. Roberts, in 1969, motivated by some problems in ecology. • By the later part of eighties, the research in this area had diminished. & 4 % ' $ An Equivalent Combinatorial Problem • The boxicity(G) is the same as the minimum number k such that there exist interval graphs I1,I2,...,Ik such that G = I1 ∩ I2 ∩···∩ Ik. -
Box Representations of Embedded Graphs
Box representations of embedded graphs Louis Esperet CNRS, Laboratoire G-SCOP, Grenoble, France S´eminairede G´eom´etrieAlgorithmique et Combinatoire, Paris March 2017 Definition (Roberts 1969) The boxicity of a graph G, denoted by box(G), is the smallest d such that G is the intersection graph of some d-boxes. Ecological/food chain networks Sociological/political networks Fleet maintenance Boxicity d-box: the cartesian product of d intervals [x1; y1] ::: [xd ; yd ] of R × × Ecological/food chain networks Sociological/political networks Fleet maintenance Boxicity d-box: the cartesian product of d intervals [x1; y1] ::: [xd ; yd ] of R × × Definition (Roberts 1969) The boxicity of a graph G, denoted by box(G), is the smallest d such that G is the intersection graph of some d-boxes. Ecological/food chain networks Sociological/political networks Fleet maintenance Boxicity d-box: the cartesian product of d intervals [x1; y1] ::: [xd ; yd ] of R × × Definition (Roberts 1969) The boxicity of a graph G, denoted by box(G), is the smallest d such that G is the intersection graph of some d-boxes. Ecological/food chain networks Sociological/political networks Fleet maintenance Boxicity d-box: the cartesian product of d intervals [x1; y1] ::: [xd ; yd ] of R × × Definition (Roberts 1969) The boxicity of a graph G, denoted by box(G), is the smallest d such that G is the intersection graph of some d-boxes. Ecological/food chain networks Sociological/political networks Fleet maintenance Boxicity d-box: the cartesian product of d intervals [x1; y1] ::: [xd ; yd ] of R × × Definition (Roberts 1969) The boxicity of a graph G, denoted by box(G), is the smallest d such that G is the intersection graph of some d-boxes. -
Sub-Coloring and Hypo-Coloring Interval Graphs⋆
Sub-coloring and Hypo-coloring Interval Graphs? Rajiv Gandhi1, Bradford Greening, Jr.1, Sriram Pemmaraju2, and Rajiv Raman3 1 Department of Computer Science, Rutgers University-Camden, Camden, NJ 08102. E-mail: [email protected]. 2 Department of Computer Science, University of Iowa, Iowa City, Iowa 52242. E-mail: [email protected]. 3 Max-Planck Institute for Informatik, Saarbr¨ucken, Germany. E-mail: [email protected]. Abstract. In this paper, we study the sub-coloring and hypo-coloring problems on interval graphs. These problems have applications in job scheduling and distributed computing and can be used as “subroutines” for other combinatorial optimization problems. In the sub-coloring problem, given a graph G, we want to partition the vertices of G into minimum number of sub-color classes, where each sub-color class induces a union of disjoint cliques in G. In the hypo-coloring problem, given a graph G, and integral weights on vertices, we want to find a partition of the vertices of G into sub-color classes such that the sum of the weights of the heaviest cliques in each sub-color class is minimized. We present a “forbidden subgraph” characterization of graphs with sub-chromatic number k and use this to derive a a 3-approximation algorithm for sub-coloring interval graphs. For the hypo-coloring problem on interval graphs, we first show that it is NP-complete and then via reduction to the max-coloring problem, show how to obtain an O(log n)-approximation algorithm for it. 1 Introduction Given a graph G = (V, E), a k-sub-coloring of G is a partition of V into sub-color classes V1,V2,...,Vk; a subset Vi ⊆ V is called a sub-color class if it induces a union of disjoint cliques in G. -
Combinatorialalgorithms for Packings, Coverings and Tilings Of
Departm en t of Com m u n ication s an d Networkin g Aa lto- A s hi k M a thew Ki zha k k ep a lla thu DD 112 Combinatorial Algorithms / 2015 for Packings, Coverings and Tilings of Hypercubes Combinatorial Algorithms for Packings, Coverings and Tilings of Hypercubes Hypercubes of Tilings and Coverings Packings, for Algorithms Combinatorial Ashik Mathew Kizhakkepallathu 9HSTFMG*agdcgd+ 9HSTFMG*agdcgd+ ISBN 978-952-60-6326-3 (printed) BUSINESS + ISBN 978-952-60-6327-0 (pdf) ECONOMY ISSN-L 1799-4934 ISSN 1799-4934 (printed) ART + ISSN 1799-4942 (pdf) DESIGN + ARCHITECTURE Aalto Un iversity Aalto University School of Electrical Engineering SCIENCE + Department of Communications and Networking TECHNOLOGY www.aalto.fi CROSSOVER DOCTORAL DOCTORAL DISSERTATIONS DISSERTATIONS Aalto University publication series DOCTORAL DISSERTATIONS 112/2015 Combinatorial Algorithms for Packings, Coverings and Tilings of Hypercubes Ashik Mathew Kizhakkepallathu A doctoral dissertation completed for the degree of Doctor of Science (Technology) to be defended, with the permission of the Aalto University School of Electrical Engineering, at a public examination held at the lecture hall S1 of the school on 18 September 2015 at 12. Aalto University School of Electrical Engineering Department of Communications and Networking Information Theory Supervising professor Prof. Patric R. J. Östergård Preliminary examiners Dr. Mathieu Dutour Sikirić, Ruđer Bošković Institute, Croatia Prof. Aleksander Vesel, University of Maribor, Slovenia Opponent Prof. Sándor Szabó, University of Pécs, Hungary Aalto University publication series DOCTORAL DISSERTATIONS 112/2015 © Ashik Mathew Kizhakkepallathu ISBN 978-952-60-6326-3 (printed) ISBN 978-952-60-6327-0 (pdf) ISSN-L 1799-4934 ISSN 1799-4934 (printed) ISSN 1799-4942 (pdf) http://urn.fi/URN:ISBN:978-952-60-6327-0 Unigrafia Oy Helsinki 2015 Finland Abstract Aalto University, P.O. -
Shrub-Depth a Successful Depth Measure for Dense Graphs Graphs Petr Hlinˇen´Y Faculty of Informatics, Masaryk University Brno, Czech Republic
page.19 Shrub-Depth a successful depth measure for dense graphs graphs Petr Hlinˇen´y Faculty of Informatics, Masaryk University Brno, Czech Republic Petr Hlinˇen´y, Sparsity, Logic . , Warwick, 2018 1 / 19 Shrub-depth measure for dense graphs page.19 Shrub-Depth a successful depth measure for dense graphs graphs Petr Hlinˇen´y Faculty of Informatics, Masaryk University Brno, Czech Republic Ingredients: joint results with J. Gajarsk´y,R. Ganian, O. Kwon, J. Neˇsetˇril,J. Obdrˇz´alek, S. Ordyniak, P. Ossona de Mendez Petr Hlinˇen´y, Sparsity, Logic . , Warwick, 2018 1 / 19 Shrub-depth measure for dense graphs page.19 Measuring Width or Depth? • Being close to a TREE { \•-width" sparse dense tree-width / branch-width { showing a structure clique-width / rank-width { showing a construction Petr Hlinˇen´y, Sparsity, Logic . , Warwick, 2018 2 / 19 Shrub-depth measure for dense graphs page.19 Measuring Width or Depth? • Being close to a TREE { \•-width" sparse dense tree-width / branch-width { showing a structure clique-width / rank-width { showing a construction • Being close to a STAR { \•-depth" sparse dense tree-depth { containment in a structure ??? (will show) Petr Hlinˇen´y, Sparsity, Logic . , Warwick, 2018 2 / 19 Shrub-depth measure for dense graphs page.19 1 Recall: Width Measures Tree-width tw(G) ≤ k if whole G can be covered by bags of size ≤ k + 1, arranged in a \tree-like fashion". Petr Hlinˇen´y, Sparsity, Logic . , Warwick, 2018 3 / 19 Shrub-depth measure for dense graphs page.19 1 Recall: Width Measures Tree-width tw(G) ≤ k if whole G can be covered by bags of size ≤ k + 1, arranged in a \tree-like fashion". -