Quasipolynomial Representation of Transversal Matroids with Applications in Parameterized Complexity
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Even Factors, Jump Systems, and Discrete Convexity
Even Factors, Jump Systems, and Discrete Convexity Yusuke KOBAYASHI¤ Kenjiro TAKAZAWAy May, 2007 Abstract A jump system, which is a set of integer lattice points with an exchange property, is an extended concept of a matroid. Some combinatorial structures such as the degree sequences of the matchings in an undirected graph are known to form a jump system. On the other hand, the maximum even factor problem is a generalization of the maximum matching problem into digraphs. When the given digraph has a certain property called odd- cycle-symmetry, this problem is polynomially solvable. The main result of this paper is that the degree sequences of all even factors in a digraph form a jump system if and only if the digraph is odd-cycle-symmetric. Furthermore, as a gen- eralization, we show that the weighted even factors induce M-convex (M-concave) functions on jump systems. These results suggest that even factors are a natural generalization of matchings and the assumption of odd-cycle-symmetry of digraphs is essential. 1 Introduction In the study of combinatorial optimization, extensions of matroids are introduced as abstract con- cepts including many combinatorial objects. A number of optimization problems on matroidal structures can be solved in polynomial time. One of the extensions of matroids is a jump system of Bouchet and Cunningham [2]. A jump system is a set of integer lattice points with an exchange property (to be described in Section 2.1); see also [18, 22]. It is a generalization of a matroid [4], a delta-matroid [1, 3, 8], and a base polyhedron of an integral polymatroid (or a submodular sys- tem) [14]. -
Some Topics Concerning Graphs, Signed Graphs and Matroids
SOME TOPICS CONCERNING GRAPHS, SIGNED GRAPHS AND MATROIDS DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of the Ohio State University By Vaidyanathan Sivaraman, M.S. Graduate Program in Mathematics The Ohio State University 2012 Dissertation Committee: Prof. Neil Robertson, Advisor Prof. Akos´ Seress Prof. Matthew Kahle ABSTRACT We discuss well-quasi-ordering in graphs and signed graphs, giving two short proofs of the bounded case of S. B. Rao's conjecture. We give a characterization of graphs whose bicircular matroids are signed-graphic, thus generalizing a theorem of Matthews from the 1970s. We prove a recent conjecture of Zaslavsky on the equality of frus- tration number and frustration index in a certain class of signed graphs. We prove that there are exactly seven signed Heawood graphs, up to switching isomorphism. We present a computational approach to an interesting conjecture of D. J. A. Welsh on the number of bases of matroids. We then move on to study the frame matroids of signed graphs, giving explicit signed-graphic representations of certain families of matroids. We also discuss the cycle, bicircular and even-cycle matroid of a graph and characterize matroids arising as two different such structures. We study graphs in which any two vertices have the same number of common neighbors, giving a quick proof of Shrikhande's theorem. We provide a solution to a problem of E. W. Dijkstra. Also, we discuss the flexibility of graphs on the projective plane. We conclude by men- tioning partial progress towards characterizing signed graphs whose frame matroids are transversal, and some miscellaneous results. -
Quasipolynomial Representation of Transversal Matroids with Applications in Parameterized Complexity
Quasipolynomial Representation of Transversal Matroids with Applications in Parameterized Complexity Daniel Lokshtanov1, Pranabendu Misra2, Fahad Panolan1, Saket Saurabh1,2, and Meirav Zehavi3 1 University of Bergen, Bergen, Norway. {daniello,pranabendu.misra,fahad.panolan}@ii.uib.no 2 The Institute of Mathematical Sciences, HBNI, Chennai, India. [email protected] 3 Ben-Gurion University, Beersheba, Israel. [email protected] Abstract Deterministic polynomial-time computation of a representation of a transversal matroid is a longstanding open problem. We present a deterministic computation of a so-called union rep- resentation of a transversal matroid in time quasipolynomial in the rank of the matroid. More precisely, we output a collection of linear matroids such that a set is independent in the trans- versal matroid if and only if it is independent in at least one of them. Our proof directly implies that if one is interested in preserving independent sets of size at most r, for a given r ∈ N, but does not care whether larger independent sets are preserved, then a union representation can be computed deterministically in time quasipolynomial in r. This consequence is of independent interest, and sheds light on the power of union representation. Our main result also has applications in Parameterized Complexity. First, it yields a fast computation of representative sets, and due to our relaxation in the context of r, this computation also extends to (standard) truncations. In turn, this computation enables to efficiently solve various problems, such as subcases of subgraph isomorphism, motif search and packing problems, in the presence of color lists. Such problems have been studied to model scenarios where pairs of elements to be matched may not be identical but only similar, and color lists aim to describe the set of compatible elements associated with each element. -
Partial Fields and Matroid Representation
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by UC Research Repository Partial Fields and Matroid Representation Charles Semple and Geoff Whittle Department of Mathematics Victoria University PO Box 600 Wellington New Zealand April 10, 1995 Abstract A partial field P is an algebraic structure that behaves very much like a field except that addition is a partial binary operation, that is, for some a; b ∈ P, a + b may not be defined. We develop a theory of matroid representation over partial fields. It is shown that many im- portant classes of matroids arise as the class of matroids representable over a partial field. The matroids representable over a partial field are closed under standard matroid operations such as the taking of mi- nors, duals, direct sums and 2{sums. Homomorphisms of partial fields are defined. It is shown that if ' : P1 → P2 is a non-trivial partial field homomorphism, then every matroid representable over P1 is rep- resentable over P2. The connection with Dowling group geometries is examined. It is shown that if G is a finite abelian group, and r>2, then there exists a partial field over which the rank{r Dowling group geometry is representable if and only if G has at most one element of order 2, that is, if G is a group in which the identity has at most two square roots. 1 1 Introduction It follows from a classical (1958) result of Tutte [19] that a matroid is rep- resentable over GF (2) and some field of characteristic other than 2 if and only if it can be represented over the rationals by the columns of a totally unimodular matrix, that is, by a matrix over the rationals all of whose non- zero subdeterminants are in {1; −1}. -
Invitation to Matroid Theory
Invitation to Matroid Theory Gregory Henselman-Petrusek January 21, 2021 Abstract This text is a companion to the short course Invitation to matroid theory taught in the Univer- sity of Oxford Centre for TDA in January 2021. It was first written for algebraic topologists, but should be suitable for all audiences; no background in matroid theory is assumed! 1 How to use this text Matroid theory is a beautiful, powerful, and accessible. Many important subjects can be understood and even researched with just a handful of concepts and definitions from matroid theory. Even so, getting use out of matroids often depends on knowing the right keywords for an internet search, and finding the right keyword depends on two things: (i) a precise knowledge of the core terms/definitions of matroid theory, and (ii) a birds eye view of the network of relationships that interconnect some of the main ideas in the field. These are the focus of this text; it falls far short of a comprehensive overview, but provides a strong start to (i) and enough of (ii) to build an interesting (and fun!) network of ideas including some of the most important concepts in matroid theory. The proofs of most results can be found in standard textbooks (e.g. Oxley, Matroid theory). Exercises are chosen to maximize the fraction intellectual reward time required to solve Therefore, if an exercise takes more than a few minutes to solve, it is fine to look up answers in a textbook! Struggling is not prerequisite to learning, at this level. 2 References The following make no attempt at completeness. -
Parameterized Algorithms Using Matroids Lecture I: Matroid Basics and Its Use As Data Structure
Parameterized Algorithms using Matroids Lecture I: Matroid Basics and its use as data structure Saket Saurabh The Institute of Mathematical Sciences, India and University of Bergen, Norway, ADFOCS 2013, MPI, August 5{9, 2013 1 Introduction and Kernelization 2 Fixed Parameter Tractable (FPT) Algorithms For decision problems with input size n, and a parameter k, (which typically is the solution size), the goal here is to design an algorithm with (1) running time f (k) nO , where f is a function of k alone. · Problems that have such an algorithm are said to be fixed parameter tractable (FPT). 3 A Few Examples Vertex Cover Input: A graph G = (V ; E) and a positive integer k. Parameter: k Question: Does there exist a subset V 0 V of size at most k such ⊆ that for every edge( u; v) E either u V 0 or v V 0? 2 2 2 Path Input: A graph G = (V ; E) and a positive integer k. Parameter: k Question: Does there exist a path P in G of length at least k? 4 Kernelization: A Method for Everyone Informally: A kernelization algorithm is a polynomial-time transformation that transforms any given parameterized instance to an equivalent instance of the same problem, with size and parameter bounded by a function of the parameter. 5 Kernel: Formally Formally: A kernelization algorithm, or in short, a kernel for a parameterized problem L Σ∗ N is an algorithm that given ⊆ × (x; k) Σ∗ N, outputs in p( x + k) time a pair( x 0; k0) Σ∗ N such that 2 × j j 2 × • (x; k) L (x 0; k0) L , 2 () 2 • x 0 ; k0 f (k), j j ≤ where f is an arbitrary computable function, and p a polynomial. -
Matroid Theory Release 9.4
Sage 9.4 Reference Manual: Matroid Theory Release 9.4 The Sage Development Team Aug 24, 2021 CONTENTS 1 Basics 1 2 Built-in families and individual matroids 77 3 Concrete implementations 97 4 Abstract matroid classes 149 5 Advanced functionality 161 6 Internals 173 7 Indices and Tables 197 Python Module Index 199 Index 201 i ii CHAPTER ONE BASICS 1.1 Matroid construction 1.1.1 Theory Matroids are combinatorial structures that capture the abstract properties of (linear/algebraic/...) dependence. For- mally, a matroid is a pair M = (E; I) of a finite set E, the groundset, and a collection of subsets I, the independent sets, subject to the following axioms: • I contains the empty set • If X is a set in I, then each subset of X is in I • If two subsets X, Y are in I, and jXj > jY j, then there exists x 2 X − Y such that Y + fxg is in I. See the Wikipedia article on matroids for more theory and examples. Matroids can be obtained from many types of mathematical structures, and Sage supports a number of them. There are two main entry points to Sage’s matroid functionality. The object matroids. contains a number of con- structors for well-known matroids. The function Matroid() allows you to define your own matroids from a variety of sources. We briefly introduce both below; follow the links for more comprehensive documentation. Each matroid object in Sage comes with a number of built-in operations. An overview can be found in the documen- tation of the abstract matroid class. -
Lecture 13 — February, 1 2014 1 Overview 2 Matroids
Advanced Graph Algorithms Jan-Apr 2014 Lecture 13 | February, 1 2014 Lecturer: Saket Saurabh Scribe: Sanjukta Roy 1 Overview In this lecture we learn what is Matroid, the connection between greedy algorithms and matroids. We also look at some examples of Matroids e.g., Linear Matroids, Graphic Matroids etc. 2 Matroids 2.1 A Greedy Approach Let G = (V, E) be a connected undirected graph and let w : E ! R≥0 be a weight function on the edges. For MWST Kruskal's so-called greedy algorithm works. Consider Maximum Weight Matching problem. 1 a b 3 3 d c 4 Application of the greedy algorithm gives (d,c) and (a,b). However, (d,c) and (a,b) do not form a matching of maximum weight. It is obviously not true that such a greedy approach would lead to an optimal solution for any combinatorial optimization problem. It turns out that the structures for which the greedy algorithm does lead to an optimal solution, are the matroids. 2.2 Matroids Definition 1. A pair M = (E, I), where E is a ground set and I is a family of subsets (called independent sets) of E, is a matroid if it satisfies the following conditions: (I1) φ 2 IorI = ø: 1 (I2) If A0 ⊆ A and A 2 I then A0 2 I. (I3) If A, B 2 I and jAj < jBj, then 9e 2 (B n A) such that A [feg 2 I: The axiom (I2) is also called the hereditary property and a pair M = (E, I) satisfying (I1) and (I2) is called hereditary family or set-family. -
Partial Fields and Matroid Representation
ADVANCES IN APPLIED MATHEMATICS 17, 184]208Ž. 1996 ARTICLE NO. 0010 Partial Fields and Matroid Representation Charles Semple and Geoff Whittle Department of Mathematics, Victoria Uni¨ersity, PO Box 600 Wellington, New Zealand Received May 12, 1995 A partial field P is an algebraic structure that behaves very much like a field except that addition is a partial binary operation, that is, for some a, b g P, a q b may not be defined. We develop a theory of matroid representation over partial fields. It is shown that many important classes of matroids arise as the class of matroids representable over a partial field. The matroids representable over a partial field are closed under standard matroid operations such as the taking of minors, duals, direct sums, and 2-sums. Homomorphisms of partial fields are defined. It is shown that if w: P12ª P is a non-trivial partial-field homomorphism, then every matroid representable over P12is representable over P . The connec- tion with Dowling group geometries is examined. It is shown that if G is a finite abelian group, and r ) 2, then there exists a partial field over which the rank-r Dowling group geometry is representable if and only if G has at most one element of order 2, that is, if G is a group in which the identity has at most two square roots. Q 1996 Academic Press, Inc. 1. INTRODUCTION It follows from a classical result of Tuttewx 19 that a matroid is repre- sentable over GFŽ.2 and some field of characteristic other than 2 if and only if it can be represented over the rationals by the columns of a totally unimodular matrix, that is, by a matrix over the rationals all of whose non-zero subdeterminants are inÄ4 1, y1 . -
Matroid Matching: the Power of Local Search
Matroid Matching: the Power of Local Search [Extended Abstract] Jon Lee Maxim Sviridenko Jan Vondrák IBM Research IBM Research IBM Research Yorktown Heights, NY Yorktown Heights, NY San Jose, CA [email protected] [email protected] [email protected] ABSTRACT can be easily transformed into an NP-completeness proof We consider the classical matroid matching problem. Un- for a concrete class of matroids (see [46]). An important weighted matroid matching for linear matroids was solved result of Lov´asz is that (unweighted) matroid matching can by Lov´asz, and the problem is known to be intractable for be solved in polynomial time for linear matroids (see [35]). general matroids. We present a PTAS for unweighted ma- There have been several attempts to generalize Lov´asz' re- troid matching for general matroids. In contrast, we show sult to the weighted case. Polynomial-time algorithms are that natural LP relaxations have an Ω(n) integrality gap known for some special cases (see [49]), but for general linear and moreover, Ω(n) rounds of the Sherali-Adams hierarchy matroids there is only a pseudopolynomial-time randomized are necessary to bring the gap down to a constant. exact algorithm (see [8, 40]). More generally, for any fixed k ≥ 2 and > 0, we obtain a In this paper, we revisit the matroid matching problem (k=2 + )-approximation for matroid matching in k-uniform for general matroids. Our main result is that while LP- hypergraphs, also known as the matroid k-parity problem. based approaches including the Sherali-Adams hierarchy fail As a consequence, we obtain a (k=2 + )-approximation for to provide any meaningful approximation, a simple local- the problem of finding the maximum-cardinality set in the search algorithm gives a PTAS (in the unweighted case). -
Matroid Representation of Clique Complexes∗
Matroid representation of clique complexes¤ x Kenji Kashiwabaray Yoshio Okamotoz Takeaki Uno{ Abstract In this paper, we approach the quality of a greedy algorithm for the maximum weighted clique problem from the viewpoint of matroid theory. More precisely, we consider the clique complex of a graph (the collection of all cliques of the graph) which is also called a flag complex, and investigate the minimum number k such that the clique complex of a given graph can be represented as the intersection of k matroids. This number k can be regarded as a measure of “how complex a graph is with respect to the maximum weighted clique problem” since a greedy algorithm is a k-approximation algorithm for this problem. For any k > 0, we characterize graphs whose clique complexes can be represented as the intersection of k matroids. As a consequence, we can see that the class of clique complexes is the same as the class of the intersections of partition matroids. Moreover, we determine how many matroids are necessary and sufficient for the representation of all graphs with n vertices. This number turns out to be n - 1. Other related investigations are also given. Keywords: Abstract simplicial complex, Clique complex, Flag complex, Independence system, Matroid intersec- tion, Partition matroid 1 Introduction An independence system is a family of subsets of a nonempty finite set such that all subsets of a member of the family are also members of the family. A lot of combinatorial optimization problems can be seen as optimization problems on the corresponding independence systems. For example, in the minimum cost spanning tree problem, we want to find a maximal set with minimum total weight in the collection of all forests of a given graph, which is an independence system. -
Small Space Stream Summary for Matroid Center Sagar Kale EPFL, Lausanne, Switzerland Sagar.Kale@Epfl.Ch
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Dagstuhl Research Online Publication Server Small Space Stream Summary for Matroid Center Sagar Kale EPFL, Lausanne, Switzerland sagar.kale@epfl.ch Abstract In the matroid center problem, which generalizes the k-center problem, we need to pick a set of centers that is an independent set of a matroid with rank r. We study this problem in streaming, where elements of the ground set arrive in the stream. We first show that any randomized one-pass streaming algorithm that computes a better than ∆-approximation for partition-matroid center must use Ω(r2) bits of space, where ∆ is the aspect ratio of the metric and can be arbitrarily large. This shows a quadratic separation between matroid center and k-center, for which the Doubling algorithm [7] gives an 8-approximation using O(k)-space and one pass. To complement this, we give a one-pass algorithm for matroid center that stores at most O(r2 log(1/ε)/ε) points (viz., stream summary) among which a (7 + ε)-approximate solution exists, which can be found by brute force, or a (17 + ε)-approximation can be found with an efficient algorithm. If we are allowed a second pass, we can compute a (3 + ε)-approximation efficiently. We also consider the problem of matroid center with z outliers and give a one-pass algorithm that outputs a set of O((r2 + rz) log(1/ε)/ε) points that contains a (15 + ε)-approximate solution. Our techniques extend to knapsack center and knapsack center with z outliers in a straightforward way, and we get algorithms that use space linear in the size of a largest feasible set (as opposed to quadratic space for matroid center).