The Bidimensionality Theory and Its Algorithmic
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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. -
A Unified Polynomial-Time Algorithm for Feedback Vertex Set on Graphs
A Unified Polynomial-Time Algorithm for Feedback Vertex Set on Graphs of Bounded Mim-Width Lars Jaffke Department of Informatics, University of Bergen, Norway lars.jaff[email protected] O-joung Kwon Logic and Semantics, Technische Universität Berlin, Berlin, Germany [email protected] Jan Arne Telle Department of Informatics, University of Bergen, Norway [email protected] Abstract We give a first polynomial-time algorithm for (Weighted) Feedback Vertex Set on graphs of bounded maximum induced matching width (mim-width). Explicitly, given a branch decom- position of mim-width w, we give an nO(w)-time algorithm that solves Feedback Vertex Set. This provides a unified algorithm for many well-known classes, such as Interval graphs and Permutation graphs, and furthermore, it gives the first polynomial-time algorithms for other classes of bounded mim-width, such as Circular Permutation and Circular k-Trapezoid graphs for fixed k. In all these classes the decomposition is computable in polynomial time, as shown by Belmonte and Vatshelle [Theor. Comput. Sci. 2013]. We show that powers of graphs of tree-width w − 1 or path-width w and powers of graphs of clique-width w have mim-width at most w. These results extensively provide new classes of bounded mim-width. We prove a slight strengthening of the first statement which implies that, surprisingly, Leaf Power graphs which are of importance in the field of phylogenetic studies have mim-width at most 1. Given a tree decomposition of width w − 1, a path decomposition of width w, or a clique-width w-expression of a graph G, one can for any value of k find a mim-width decomposition of its k-power in polynomial time, and apply our algorithm to solve Feedback Vertex Set on the k-power in time nO(w). -
Treewidth I (Algorithms and Networks)
Treewidth Algorithms and Networks Overview • Historic introduction: Series parallel graphs • Dynamic programming on trees • Dynamic programming on series parallel graphs • Treewidth • Dynamic programming on graphs of small treewidth • Finding tree decompositions 2 Treewidth Computing the Resistance With the Laws of Ohm = + 1 1 1 1789-1854 R R1 R2 = + R R1 R2 R1 R2 R1 Two resistors in series R2 Two resistors in parallel 3 Treewidth Repeated use of the rules 6 6 5 2 2 1 7 Has resistance 4 1/6 + 1/2 = 1/(1.5) 1.5 + 1.5 + 5 = 8 1 + 7 = 8 1/8 + 1/8 = 1/4 4 Treewidth 5 6 6 5 2 2 1 7 A tree structure tree A 5 6 P S 2 P 6 P 2 7 S Treewidth 1 Carry on! • Internal structure of 6 6 5 graph can be forgotten 2 2 once we know essential information 1 7 about it! 4 ¼ + ¼ = ½ 6 Treewidth Using tree structures for solving hard problems on graphs 1 • Network is ‘series parallel graph’ • 196*, 197*: many problems that are hard for general graphs are easy for e.g.: – Trees NP-complete – Series parallel graphs • Many well-known problems Linear / polynomial time computable 7 Treewidth Weighted Independent Set • Independent set: set of vertices that are pair wise non-adjacent. • Weighted independent set – Given: Graph G=(V,E), weight w(v) for each vertex v. – Question: What is the maximum total weight of an independent set in G? • NP-complete 8 Treewidth Weighted Independent Set on Trees • On trees, this problem can be solved in linear time with dynamic programming. -
A Fixed-Parameter Algorithm for the Directed Feedback Vertex Set Problem
A Fixed-Parameter Algorithm for the Directed Feedback Vertex Set Problem Jianer Chen, Yang Liu, Songjian Lu Barry O’Sullivan and Igor Razgon Department of Computer Science Cork Constraint Computation Centre Texas A&M University Computer Science Department College Station, TX 77843, USA University College Cork, Ireland {chen,yangliu,sjlu}@cs.tamu.edu {b.osullivan,i.razgon}@cs.ucc.ie ABSTRACT played an essential role in the study of deadlock recovery in database systems and in operating systems [30, 14]. In such The (parameterized) feedback vertex set problem on di- a system, the status of system resource allocations can be rected graphs, which we refer to as the dfvs problem, is defined as follows: given a directed graph G and a param- represented as a directed graph G (i.e., the system resource- eter k, either construct a feedback vertex set of at most k allocation graph), and a directed cycle in G represents a vertices in G or report that no such set exists. Whether or deadlock in the system. Therefore, in order to recover from deadlocks, we need to abort a set of processes in the system, not the dfvs problem is fixed-parameter tractable has been a well-known open problem in parameterized computation i.e., to remove a set of vertices in the graph G, so that all and complexity, i.e., whether the problem can be solved in directed cycles in G are broken. Equivalently, we need to time f(k)nO(1) for some function f. In this paper we develop find a FVS in the graph G. -
Constrained Representations of Map Graphs and Half-Squares
Constrained Representations of Map Graphs and Half-Squares Hoang-Oanh Le Berlin, Germany [email protected] Van Bang Le Universität Rostock, Institut für Informatik, Rostock, Germany [email protected] Abstract The square of a graph H, denoted H2, is obtained from H by adding new edges between two distinct vertices whenever their distance in H is two. The half-squares of a bipartite graph B = (X, Y, EB ) are the subgraphs of B2 induced by the color classes X and Y , B2[X] and B2[Y ]. For a given graph 2 G = (V, EG), if G = B [V ] for some bipartite graph B = (V, W, EB ), then B is a representation of G and W is the set of points in B. If in addition B is planar, then G is also called a map graph and B is a witness of G [Chen, Grigni, Papadimitriou. Map graphs. J. ACM, 49 (2) (2002) 127-138]. While Chen, Grigni, Papadimitriou proved that any map graph G = (V, EG) has a witness with at most 3|V | − 6 points, we show that, given a map graph G and an integer k, deciding if G admits a witness with at most k points is NP-complete. As a by-product, we obtain NP-completeness of edge clique partition on planar graphs; until this present paper, the complexity status of edge clique partition for planar graphs was previously unknown. We also consider half-squares of tree-convex bipartite graphs and prove the following complexity 2 dichotomy: Given a graph G = (V, EG) and an integer k, deciding if G = B [V ] for some tree-convex bipartite graph B = (V, W, EB ) with |W | ≤ k points is NP-complete if G is non-chordal dually chordal and solvable in linear time otherwise. -
FPT Algorithms for Diverse Collections of Hitting Sets
algorithms Article FPT Algorithms for Diverse Collections of Hitting Sets Julien Baste 1,* , Lars Jaffke 2,*, Tomáš Masaˇrík 3,4,* , Geevarghese Philip 5,6,* and Günter Rote 7,* 1 Institute of Optimization and Operations Research, Ulm University, 89081 Ulm, Germany 2 Department of Informatics, University of Bergen, 5008 Bergen, Norway 3 Department of Applied Mathematics of the Faculty of Mathematics and Physics, Charles University, 11800 Prague, Czech Republic 4 Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, 02-097 Warszawa, Poland 5 Chennai Mathematical Institute, Chennai 603103, India 6 International Joint Unit Research Lab in Computer Science (UMI ReLaX), Chennai 603103, India 7 Fachbereich Mathematik und Informatik, Freie Universität Berlin, D-14195 Berlin, Germany * Correspondence: [email protected] (J.B.); [email protected] (L.J.); [email protected] (T.M.); [email protected] (G.P.); [email protected] (G.R.) Received: 25 October 2019; Accepted: 23 November 2019; Published: 27 November 2019 Abstract: In this work, we study the d-HITTING SET and FEEDBACK VERTEX SET problems through the paradigm of finding diverse collections of r solutions of size at most k each, which has recently been introduced to the field of parameterized complexity. This paradigm is aimed at addressing the loss of important side information which typically occurs during the abstraction process that models real-world problems as computational problems. We use two measures for the diversity of such a collection: the sum of all pairwise Hamming distances, and the minimum pairwise Hamming distance. We show that both problems are fixed-parameter tractable in k + r for both diversity measures. -
Arxiv:2006.06067V2 [Math.CO] 4 Jul 2021
Treewidth versus clique number. I. Graph classes with a forbidden structure∗† Cl´ement Dallard1 Martin Milaniˇc1 Kenny Storgelˇ 2 1 FAMNIT and IAM, University of Primorska, Koper, Slovenia 2 Faculty of Information Studies, Novo mesto, Slovenia [email protected] [email protected] [email protected] Treewidth is an important graph invariant, relevant for both structural and algo- rithmic reasons. A necessary condition for a graph class to have bounded treewidth is the absence of large cliques. We study graph classes closed under taking induced subgraphs in which this condition is also sufficient, which we call (tw,ω)-bounded. Such graph classes are known to have useful algorithmic applications related to variants of the clique and k-coloring problems. We consider six well-known graph containment relations: the minor, topological minor, subgraph, induced minor, in- duced topological minor, and induced subgraph relations. For each of them, we give a complete characterization of the graphs H for which the class of graphs excluding H is (tw,ω)-bounded. Our results yield an infinite family of χ-bounded induced-minor-closed graph classes and imply that the class of 1-perfectly orientable graphs is (tw,ω)-bounded, leading to linear-time algorithms for k-coloring 1-perfectly orientable graphs for every fixed k. This answers a question of Breˇsar, Hartinger, Kos, and Milaniˇc from 2018, and one of Beisegel, Chudnovsky, Gurvich, Milaniˇc, and Servatius from 2019, respectively. We also reveal some further algorithmic implications of (tw,ω)- boundedness related to list k-coloring and clique problems. In addition, we propose a question about the complexity of the Maximum Weight Independent Set prob- lem in (tw,ω)-bounded graph classes and prove that the problem is polynomial-time solvable in every class of graphs excluding a fixed star as an induced minor. -
Fast Dynamic Programming on Graph Decompositions
Fast Dynamic Programming on Graph Decompositions∗ Johan M. M. van Rooij† Hans L. Bodlaender† Erik Jan van Leeuwen‡ Peter Rossmanith§ Martin Vatshelle‡ June 6, 2018 Abstract In this paper, we consider three types of graph decompositions, namely tree decompo- sitions, branch decompositions, and clique decompositions. We improve the running time of dynamic programming algorithms on these graph decompositions for a large number of problems as a function of the treewidth, branchwidth, or cliquewidth, respectively. Such algorithms have many practical and theoretical applications. On tree decompositions of width k, we improve the running time for Dominating Set to O∗(3k). Hereafter, we give a generalisation of this result to [ρ,σ]-domination problems with finite or cofinite ρ and σ. For these problems, we give O∗(sk)-time algorithms. Here, s is the number of ‘states’ a vertex can have in a standard dynamic programming algorithm for such a problems. Furthermore, we give an O∗(2k)-time algorithm for counting the number of perfect matchings in a graph, and generalise this result to O∗(2k)-time algorithms for many clique covering, packing, and partitioning problems. On branch decompositions of width k, we give ∗ ω k ∗ ω k an O (3 2 )-time algorithm for Dominating Set, an O (2 2 )-time algorithm for counting ∗ ω k the number of perfect matchings, and O (s 2 )-time algorithms for [ρ,σ]-domination problems involving s states with finite or cofinite ρ and σ. Finally, on clique decompositions of width k, we give O∗(4k)-time algorithms for Dominating Set, Independent Dominating Set, and Total Dominating Set. -
Handout on Vertex Separators and Low Tree-Width K-Partition
Handout on vertex separators and low tree-width k-partition January 12 and 19, 2012 Given a graph G(V; E) and a set of vertices S ⊂ V , an S-flap is the set of vertices in a connected component of the graph induced on V n S. A set S is a vertex separator if no S-flap has more than n=p 2 vertices. Lipton and Tarjan showed that every planar graph has a separator of size O( n). This was generalized by Alon, Seymour and Thomas to any family of graphs that excludes some fixed (arbitrary) subgraph H as a minor. Theorem 1 There a polynomial time algorithm that given a parameter h and an n vertex graphp G(V; E) either outputs a Kh minor, or outputs a vertex separator of size at most h hn. Corollary 2 Let G(V; E) be an arbitrary graph with nop Kh minor, and let W ⊂ V . Then one can find in polynomial time a set S of at most h hn vertices such that every S-flap contains at most jW j=2 vertices from W . Proof: The proof given in class for Theorem 1 easily extends to this setting. 2 p Corollary 3 Every graph with no Kh as a minor has treewidth O(h hn). Moreover, a tree decomposition with this treewidth can be found in polynomial time. Proof: We have seen an algorithm that given a graph of treewidth p constructs a tree decomposition of treewidth 8p. Usingp Corollary 2, that algorithm can be modified to give a tree decomposition of treewidth 8h hn in our case, and do so in polynomial time. -
Bidimensionality Theory and Algorithmic Graph Minor Theory Lecture Notes for Mohammadtaghi Hajiaghayi’S Tutorial
Bidimensionality Theory and Algorithmic Graph Minor Theory Lecture Notes for MohammadTaghi Hajiaghayi’s Tutorial Mareike Massow,∗ Jens Schmidt,† Daria Schymura,‡ Siamak Tazari§ MDS Fall School Blankensee October 2007 1 Introduction Dealing with Hard Graph Problems Many graph problems cannot be computed in polynomial time unless P = NP , which most computer scientists and mathematicians doubt. Examples are the Traveling Salesman Problem (TSP), vertex cover, and dominating set. TSP is to find a Hamiltonian cycle with the least weight in a complete weighted graph. A vertex cover of a graph is a subset of the vertices that covers all edges. A dominating set in a graph is a subset of the vertices such that each vertex is contained in the set or has a neighbour in the set. The decision problem is to answer the question whether there is a vertex cover resp. a dominating set with at most k elements. The optimization problem is to find a vertex cover resp. a dominating set of minimal size. How can we solve these problems despite their computational complexity? The four main theoretical approaches to handle NP -hard problems are the following. • Average case: Prove that an algorithm is efficient in the expected case. • Special instances: A problem is solved efficiently for a special graph class. For example, graph isomor- phism is computable in linear time on planar graphs whereas graph isomorphism in general is known to be in NP and suspected not to be in P . • Approximation algorithms: Approximate the optimal solution within a constant factor c, or even within 1 + . An algorithmic scheme that gives for each > 0 a polynomial-time (1 + )-approximation is called a polynomial-time approximation scheme (PTAS). -
The Generalized Deadlock Resolution Problem
The Generalized Deadlock Resolution Problem Kamal Jain¤ Mohammad T. Hajiaghayiy Kunal Talwarz Abstract In this paper we initiate the study of the AND-OR directed feedback vertex set problem from the view- point of approximation algorithms. This AND-OR feedback vertex set problem is motivated by a practical deadlock resolution problem that appears in the development of distributed database systems1. This prob- lem also turns out be a natural generalization of the directed feedback vertex set problem. Awerbuch and Micali [1] gave a polynomial time algorithm to find a minimal solution for this problem. Unfortunately, a minimal solution can be arbitrarily more expensive than the minimum cost solution. We show that finding the minimum cost solution is as hard as the directed steiner tree problem (and thus (log2n) hard to ap- proximate). On the positive side, we give algorithms which work well when the number of writers (AND nodes) or the number of readers (OR nodes) are small. We also consider a variant that we call permanent deadlock resolution where we cannot specify an execution order for the surviving processes; they should get completed even if they were scheduled adver- sarially. When all processes are writers (AND nodes), we give an O(log n log log n) approximation for this problem. Finally we give an LP-rounding approach and discuss some other natural variants. 1 Introduction One of the best ways to understand deadlocks in databases is the dining philosophers problem. Say there are five philosophers sitting on a circular table to eat spaghetti, with a fork between every two of them. -
Total Domination on Some Graph Operators
mathematics Article Total Domination on Some Graph Operators José M. Sigarreta Faculty of Mathematics, Autonomous University of Guerrero, Carlos E. Adame 5, Col. La Garita, C. P. 39350 Acapulco, Mexico; [email protected]; Tel.: +52-744-159-2272 Abstract: Let G = (V, E) be a graph; a set D ⊆ V is a total dominating set if every vertex v 2 V has, at least, one neighbor in D. The total domination number gt(G) is the minimum cardinality among all total dominating sets. Given an arbitrary graph G, we consider some operators on this graph; S(G), R(G), and Q(G), and we give bounds or the exact value of the total domination number of these new graphs using some parameters in the original graph G. Keywords: domination theory; total domination; graph operators 1. Introduction The study of the mathematical and structural properties of operators in graphs was initiated in Reference [1]. This study is a current research topic, mainly because of its multiple theoretical and practical applications (see References [2–6]). Motivated by the previous investigations, we work here on the total domination number of some graph operators. Other important parameters have also been studied in this type of graphs (see References [7,8]). We denote by G = (V, E) a finite simple graph of order n = jVj and size m = jEj. For any two adjacent vertices u, v 2 V, uv denotes the corresponding edge, and we denote N(v) = fu 2 V : uv 2 Eg and N[v] = N(v) [ fvg. We denote by D, d the maximum and Citation: Sigarreta, J.M.