Cyclewidth and the Grid Theorem for Perfect Matching Width of Bipartite
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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. -
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. -
Extended Branch Decomposition Graphs: Structural Comparison of Scalar Data
Eurographics Conference on Visualization (EuroVis) 2014 Volume 33 (2014), Number 3 H. Carr, P. Rheingans, and H. Schumann (Guest Editors) Extended Branch Decomposition Graphs: Structural Comparison of Scalar Data Himangshu Saikia, Hans-Peter Seidel, Tino Weinkauf Max Planck Institute for Informatics, Saarbrücken, Germany Abstract We present a method to find repeating topological structures in scalar data sets. More precisely, we compare all subtrees of two merge trees against each other – in an efficient manner exploiting redundancy. This provides pair-wise distances between the topological structures defined by sub/superlevel sets, which can be exploited in several applications such as finding similar structures in the same data set, assessing periodic behavior in time-dependent data, and comparing the topology of two different data sets. To do so, we introduce a novel data structure called the extended branch decomposition graph, which is composed of the branch decompositions of all subtrees of the merge tree. Based on dynamic programming, we provide two highly efficient algorithms for computing and comparing extended branch decomposition graphs. Several applications attest to the utility of our method and its robustness against noise. 1. Introduction • We introduce the extended branch decomposition graph: a novel data structure that describes the hierarchical decom- Structures repeat in both nature and engineering. An example position of all subtrees of a join/split tree. We abbreviate it is the symmetric arrangement of the atoms in molecules such with ‘eBDG’. as Benzene. A prime example for a periodic process is the • We provide a fast algorithm for computing an eBDG. Typ- combustion in a car engine where the gas concentration in a ical runtimes are in the order of milliseconds. -
Exploiting C-Closure in Kernelization Algorithms for Graph Problems
Exploiting c-Closure in Kernelization Algorithms for Graph Problems Tomohiro Koana Technische Universität Berlin, Algorithmics and Computational Complexity, Germany [email protected] Christian Komusiewicz Philipps-Universität Marburg, Fachbereich Mathematik und Informatik, Marburg, Germany [email protected] Frank Sommer Philipps-Universität Marburg, Fachbereich Mathematik und Informatik, Marburg, Germany [email protected] Abstract A graph is c-closed if every pair of vertices with at least c common neighbors is adjacent. The c-closure of a graph G is the smallest number such that G is c-closed. Fox et al. [ICALP ’18] defined c-closure and investigated it in the context of clique enumeration. We show that c-closure can be applied in kernelization algorithms for several classic graph problems. We show that Dominating Set admits a kernel of size kO(c), that Induced Matching admits a kernel with O(c7k8) vertices, and that Irredundant Set admits a kernel with O(c5/2k3) vertices. Our kernelization exploits the fact that c-closed graphs have polynomially-bounded Ramsey numbers, as we show. 2012 ACM Subject Classification Theory of computation → Parameterized complexity and exact algorithms; Theory of computation → Graph algorithms analysis Keywords and phrases Fixed-parameter tractability, kernelization, c-closure, Dominating Set, In- duced Matching, Irredundant Set, Ramsey numbers Funding Frank Sommer: Supported by the Deutsche Forschungsgemeinschaft (DFG), project MAGZ, KO 3669/4-1. 1 Introduction Parameterized complexity [9, 14] aims at understanding which properties of input data can be used in the design of efficient algorithms for problems that are hard in general. The properties of input data are encapsulated in the notion of a parameter, a numerical value that can be attributed to each input instance I. -
3.1 Matchings and Factors: Matchings and Covers
1 3.1 Matchings and Factors: Matchings and Covers This copyrighted material is taken from Introduction to Graph Theory, 2nd Ed., by Doug West; and is not for further distribution beyond this course. These slides will be stored in a limited-access location on an IIT server and are not for distribution or use beyond Math 454/553. 2 Matchings 3.1.1 Definition A matching in a graph G is a set of non-loop edges with no shared endpoints. The vertices incident to the edges of a matching M are saturated by M (M-saturated); the others are unsaturated (M-unsaturated). A perfect matching in a graph is a matching that saturates every vertex. perfect matching M-unsaturated M-saturated M Contains copyrighted material from Introduction to Graph Theory by Doug West, 2nd Ed. Not for distribution beyond IIT’s Math 454/553. 3 Perfect Matchings in Complete Bipartite Graphs a 1 The perfect matchings in a complete b 2 X,Y-bigraph with |X|=|Y| exactly c 3 correspond to the bijections d 4 f: X -> Y e 5 Therefore Kn,n has n! perfect f 6 matchings. g 7 Kn,n The complete graph Kn has a perfect matching iff… Contains copyrighted material from Introduction to Graph Theory by Doug West, 2nd Ed. Not for distribution beyond IIT’s Math 454/553. 4 Perfect Matchings in Complete Graphs The complete graph Kn has a perfect matching iff n is even. So instead of Kn consider K2n. We count the perfect matchings in K2n by: (1) Selecting a vertex v (e.g., with the highest label) one choice u v (2) Selecting a vertex u to match to v K2n-2 2n-1 choices (3) Selecting a perfect matching on the rest of the vertices. -
Minimal Acyclic Forbidden Minors for the Family of Graphs with Bounded Path-Width
Discrete Mathematics 127 (1994) 293-304 293 North-Holland Minimal acyclic forbidden minors for the family of graphs with bounded path-width Atsushi Takahashi, Shuichi Ueno and Yoji Kajitani Department of Electrical and Electronic Engineering, Tokyo Institute of Technology. Tokyo, 152. Japan Received 27 November 1990 Revised 12 February 1992 Abstract The graphs with bounded path-width, introduced by Robertson and Seymour, and the graphs with bounded proper-path-width, introduced in this paper, are investigated. These families of graphs are minor-closed. We characterize the minimal acyclic forbidden minors for these families of graphs. We also give estimates for the numbers of minimal forbidden minors and for the numbers of vertices of the largest minimal forbidden minors for these families of graphs. 1. Introduction Graphs we consider are finite and undirected, but may have loops and multiple edges unless otherwise specified. A graph H is a minor of a graph G if H is isomorphic to a graph obtained from a subgraph of G by contracting edges. A family 9 of graphs is said to be minor-closed if the following condition holds: If GEB and H is a minor of G then H EF. A graph G is a minimal forbidden minor for a minor-closed family F of graphs if G#8 and any proper minor of G is in 9. Robertson and Seymour proved the following deep theorems. Theorem 1.1 (Robertson and Seymour [15]). Every minor-closed family of graphs has a finite number of minimal forbidden minors. Theorem 1.2 (Robertson and Seymour [14]). -
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. -
Approximation Algorithms
Lecture 21 Approximation Algorithms 21.1 Overview Suppose we are given an NP-complete problem to solve. Even though (assuming P = NP) we 6 can’t hope for a polynomial-time algorithm that always gets the best solution, can we develop polynomial-time algorithms that always produce a “pretty good” solution? In this lecture we consider such approximation algorithms, for several important problems. Specific topics in this lecture include: 2-approximation for vertex cover via greedy matchings. • 2-approximation for vertex cover via LP rounding. • Greedy O(log n) approximation for set-cover. • Approximation algorithms for MAX-SAT. • 21.2 Introduction Suppose we are given a problem for which (perhaps because it is NP-complete) we can’t hope for a fast algorithm that always gets the best solution. Can we hope for a fast algorithm that guarantees to get at least a “pretty good” solution? E.g., can we guarantee to find a solution that’s within 10% of optimal? If not that, then how about within a factor of 2 of optimal? Or, anything non-trivial? As seen in the last two lectures, the class of NP-complete problems are all equivalent in the sense that a polynomial-time algorithm to solve any one of them would imply a polynomial-time algorithm to solve all of them (and, moreover, to solve any problem in NP). However, the difficulty of getting a good approximation to these problems varies quite a bit. In this lecture we will examine several important NP-complete problems and look at to what extent we can guarantee to get approximately optimal solutions, and by what algorithms. -
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. -
A Note on Soenergy of Stars, Bi-Stars and Double Star Graphs
BULLETIN OF THE INTERNATIONAL MATHEMATICAL VIRTUAL INSTITUTE ISSN (p) 2303-4874, ISSN (o) 2303-4955 www.imvibl.org /JOURNALS / BULLETIN Vol. 6(2016), 105-113 Former BULLETIN OF THE SOCIETY OF MATHEMATICIANS BANJA LUKA ISSN 0354-5792 (o), ISSN 1986-521X (p) A NOTE ON soENERGY OF STARS, BISTAR AND DOUBLE STAR GRAPHS S. P. Jeyakokila and P. Sumathi Abstract. Let G be a finite non trivial connected graph.In the earlier paper idegree, odegree, oidegree of a minimal dominating set of G were introduced, oEnergy of a graph with respect to the minimal dominating set was calculated in terms of idegree and odegree. Algorithm to get the soEnergy was also introduced and soEnergy was calculated for some standard graphs in the earlier papers. In this paper soEnergy of stars, bistars and double stars with respect to the given dominating set are found out. 1. INTRODUCTION Let G = (V; E) be a finite non trivial connected graph. A set D ⊂ V is a dominating set of G if every vertex in V − D is adjacent to some vertex in D.A dominating set D of G is called a minimal dominating set if no proper subset of D is a dominating set. Star graph is a tree consisting of one vertex adjacent to all the others. Bistar is a graph obtained from K2 by joining n pendent edges to both the ends of K2. Double star is the graph obtained from K2 by joining m pendent edges to one end and n pendent edges to the other end of K2. -
Total Chromatic Number of Star and Bistargraphs 1D
International Journal of Pure and Applied Mathematics Volume 117 No. 21 2017, 699-708 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu Special Issue ijpam.eu Total Chromatic Number of Star and BistarGraphs 1D. Muthuramakrishnanand 2G. Jayaraman 1Department of Mathematics, National College, Trichy, Tamil Nadu, India. [email protected] 2Research Scholar, National College, Trichy, Tamil Nadu, India. [email protected] Abstract A total coloring of a graph G is an assignment of colors to both the vertices and edges of G, such that no two adjacent or incident vertices and edges of G are assigned the same colors. In this paper, we have discussed 2 the total coloring of 푀 퐾1,푛 , 푇 퐾1,푛 , 퐿 퐾1,푛 and 퐵푛,푛and we obtained the 2 total chromatic number of 푀 퐾1,푛 , 푇 퐾1,푛 , 퐿 퐾1,푛 and 퐵푛,푛. AMS Subject Classification: 05C15 Keywords and Phrases:Middle graph, line graph, total graph, star graph, square graph, total coloring and total chromatic number. 699 International Journal of Pure and Applied Mathematics Special Issue 1. Introduction In this paper, we have chosen finite, simple and undirected graphs. Let퐺 = (푉 퐺 , 퐸 퐺 ) be a graph with the vertex set 푉(퐺) and the edge set E(퐺), respectively. In 1965, the concept of total coloring was introduced by Behzad [1] and in 1967 he [2] came out new ideology that, the total chromatic number of complete graph and complete bi-partite graph. A total coloring of퐺, is a function 푓: 푆 → 퐶, where 푆 = 푉 퐺 ∪ 퐸 퐺 and C is a set of colors to satisfies the given conditions. -
Characterizations of Graphs Without Certain Small Minors by J. Zachary
Characterizations of Graphs Without Certain Small Minors By J. Zachary Gaslowitz Dissertation Submitted to the Faculty of the Graduate School of Vanderbilt University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in Mathematics May 11, 2018 Nashville, Tennessee Approved: Mark Ellingham, Ph.D. Paul Edelman, Ph.D. Bruce Hughes, Ph.D. Mike Mihalik, Ph.D. Jerry Spinrad, Ph.D. Dong Ye, Ph.D. TABLE OF CONTENTS Page 1 Introduction . 2 2 Previous Work . 5 2.1 Planar Graphs . 5 2.2 Robertson and Seymour's Graph Minor Project . 7 2.2.1 Well-Quasi-Orderings . 7 2.2.2 Tree Decomposition and Treewidth . 8 2.2.3 Grids and Other Graphs with Large Treewidth . 10 2.2.4 The Structure Theorem and Graph Minor Theorem . 11 2.3 Graphs Without K2;t as a Minor . 15 2.3.1 Outerplanar and K2;3-Minor-Free Graphs . 15 2.3.2 Edge-Density for K2;t-Minor-Free Graphs . 16 2.3.3 On the Structure of K2;t-Minor-Free Graphs . 17 3 Algorithmic Aspects of Graph Minor Theory . 21 3.1 Theoretical Results . 21 3.2 Practical Graph Minor Containment . 22 4 Characterization and Enumeration of 4-Connected K2;5-Minor-Free Graphs 25 4.1 Preliminary Definitions . 25 4.2 Characterization . 30 4.3 Enumeration . 38 5 Characterization of Planar 4-Connected DW6-minor-free Graphs . 51 6 Future Directions . 91 BIBLIOGRAPHY . 93 1 Chapter 1 Introduction All graphs in this paper are finite and simple. Given a graph G, the vertex set of G is denoted V (G) and the edge set is denoted E(G).