Pfaffian Pairs and Parities: Counting on Linear Matroid Intersection And
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Planar Graph Perfect Matching Is in NC
2018 IEEE 59th Annual Symposium on Foundations of Computer Science Planar Graph Perfect Matching is in NC Nima Anari Vijay V. Vazirani Computer Science Department Computer Science Department Stanford University University of California, Irvine [email protected] [email protected] Abstract—Is perfect matching in NC? That is, is there a finding a perfect matching, was obtained by Karp, Upfal, and deterministic fast parallel algorithm for it? This has been an Wigderson [11]. This was followed by a somewhat simpler outstanding open question in theoretical computer science for algorithm due to Mulmuley, Vazirani, and Vazirani [17]. over three decades, ever since the discovery of RNC matching algorithms. Within this question, the case of planar graphs has The matching problem occupies an especially distinguished remained an enigma: On the one hand, counting the number position in the theory of algorithms: Some of the most of perfect matchings is far harder than finding one (the former central notions and powerful tools within this theory were is #P-complete and the latter is in P), and on the other, for discovered in the context of an algorithmic study of this NC planar graphs, counting has long been known to be in problem, including the notion of polynomial time solvability whereas finding one has resisted a solution. P In this paper, we give an NC algorithm for finding a perfect [4] and the counting class # [22]. The parallel perspective matching in a planar graph. Our algorithm uses the above- has also led to such gains: The first RNC matching algorithm stated fact about counting matchings in a crucial way. -
PSEUDO SCHUR COMPLEMENTS, PSEUDO PRINCIPAL PIVOT TRANSFORMS and THEIR INHERITANCE PROPERTIES∗ 1. Introduction. Let M ∈ R M×
Electronic Journal of Linear Algebra ISSN 1081-3810 A publication of the International Linear Algebra Society Volume 30, pp. 455-477, August 2015 ELA PSEUDO SCHUR COMPLEMENTS, PSEUDO PRINCIPAL PIVOT TRANSFORMS AND THEIR INHERITANCE PROPERTIES∗ K. BISHT† , G. RAVINDRAN‡, AND K.C. SIVAKUMAR§ Abstract. Extensions of the Schur complement and the principal pivot transform, where the usual inverses are replaced by the Moore-Penrose inverse, are revisited. These are called the pseudo Schur complement and the pseudo principal pivot transform, respectively. First, a generalization of the characterization of a block matrix to be an M-matrix is extended to the nonnegativity of the Moore-Penrose inverse. A comprehensive treatment of the fundamental properties of the extended notion of the principal pivot transform is presented. Inheritance properties with respect to certain matrix classes are derived, thereby generalizing some of the existing results. Finally, a thorough discussion on the preservation of left eigenspaces by the pseudo principal pivot transformation is presented. Key words. Principal pivot transform, Schur complement, Nonnegative Moore-Penrose inverse, P†-Matrix, R†-Matrix, Left eigenspace, Inheritance properties. AMS subject classifications. 15A09, 15A18, 15B48. 1. Introduction. Let M ∈ Rm×n be a block matrix partitioned as A B , C D where A ∈ Rk×k is nonsingular. Then the classical Schur complement of A in M denoted by M/A is given by D − CA−1B ∈ R(m−k)×(n−k). This notion has proved to be a fundamental idea in many applications like numerical analysis, statistics and operator inequalities, to name a few. We refer the reader to [27] for a comprehen- sive account of the Schur complement. -
Linear Algebraic Techniques for Spanning Tree Enumeration
LINEAR ALGEBRAIC TECHNIQUES FOR SPANNING TREE ENUMERATION STEVEN KLEE AND MATTHEW T. STAMPS Abstract. Kirchhoff's Matrix-Tree Theorem asserts that the number of spanning trees in a finite graph can be computed from the determinant of any of its reduced Laplacian matrices. In many cases, even for well-studied families of graphs, this can be computationally or algebraically taxing. We show how two well-known results from linear algebra, the Matrix Determinant Lemma and the Schur complement, can be used to elegantly count the spanning trees in several significant families of graphs. 1. Introduction A graph G consists of a finite set of vertices and a set of edges that connect some pairs of vertices. For the purposes of this paper, we will assume that all graphs are simple, meaning they do not contain loops (an edge connecting a vertex to itself) or multiple edges between a given pair of vertices. We will use V (G) and E(G) to denote the vertex set and edge set of G respectively. For example, the graph G with V (G) = f1; 2; 3; 4g and E(G) = ff1; 2g; f2; 3g; f3; 4g; f1; 4g; f1; 3gg is shown in Figure 1. A spanning tree in a graph G is a subgraph T ⊆ G, meaning T is a graph with V (T ) ⊆ V (G) and E(T ) ⊆ E(G), that satisfies three conditions: (1) every vertex in G is a vertex in T , (2) T is connected, meaning it is possible to walk between any two vertices in G using only edges in T , and (3) T does not contain any cycles. -
Formalizing Randomized Matching Algorithms
Formalizing Randomized Matching Algorithms Dai Tri Man Leˆ and Stephen A. Cook Department of Computer Science, University of Toronto fledt, [email protected] Abstract—Using Jerˇabek’s´ framework for probabilistic rea- set to another. Using this method, Jerˇabek´ developed tools for soning, we formalize the correctness of two fundamental RNC2 describing algorithms in ZPP and RP. He also showed in [13], algorithms for bipartite perfect matching within the theory VPV [14] that the theory VPV+sWPHP(L ) is powerful enough to for polytime reasoning. The first algorithm is for testing if a FP bipartite graph has a perfect matching, and is based on the formalize proofs of very sophisticated derandomization results, Schwartz-Zippel Lemma for polynomial identity testing applied e.g. the Nisan-Wigderson theorem [23] and the Impagliazzo- to the Edmonds polynomial of the graph. The second algorithm, Wigderson theorem [12]. (Note that Jerˇabek´ actually used the due to Mulmuley, Vazirani and Vazirani, is for finding a perfect single-sorted theory PV1+sWPHP(PV), but these two theories matching, where the key ingredient of this algorithm is the are isomorphic.) Isolating Lemma. In [15], Jerˇabek´ developed an even more systematic ap- proach by showing that for any bounded P=poly definable set, I. INTRODUCTION there exists a suitable pair of surjective “counting functions” 1 There is a substantial literature on theories such as PV, S2 , which can approximate the cardinality of the set up to a poly- VPV, V 1 which capture polynomial time reasoning [8], [4], nomially small error. From this and other results he argued [17], [7]. -
Structural Results on Matching Estimation with Applications to Streaming∗
Structural Results on Matching Estimation with Applications to Streaming∗ Marc Bury, Elena Grigorescu, Andrew McGregor, Morteza Monemizadeh, Chris Schwiegelshohn, Sofya Vorotnikova, Samson Zhou We study the problem of estimating the size of a matching when the graph is revealed in a streaming fashion. Our results are multifold: 1. We give a tight structural result relating the size of a maximum matching to the arboricity α of a graph, which has been one of the most studied graph parameters for matching algorithms in data streams. One of the implications is an algorithm that estimates the matching size up to a factor of (α + 2)(1 + ") using O~(αn2=3) space in insertion-only graph streams and O~(αn4=5) space in dynamic streams, where n is the number of nodes in the graph. We also show that in the vertex arrival insertion-only model, an (α + 2) approximation can be achieved using only O(log n) space. 2. We further show that the weight of a maximum weighted matching can be ef- ficiently estimated by augmenting any routine for estimating the size of an un- weighted matching. Namely, given an algorithm for computing a λ-approximation in the unweighted case, we obtain a 2(1+")·λ approximation for the weighted case, while only incurring a multiplicative logarithmic factor in the space bounds. The algorithm is implementable in any streaming model, including dynamic streams. 3. We also investigate algebraic aspects of computing matchings in data streams, by proposing new algorithms and lower bounds based on analyzing the rank of the Tutte-matrix of the graph. -
Teacher's Guide for Spanning and Weighted Spanning Trees
Teacher’s Guide for Spanning and weighted spanning trees: a different kind of optimization by sarah-marie belcastro 1TheMath. Let’s talk about spanning trees. No, actually, first let’s talk about graph theory,theareaof mathematics within which the topic of spanning trees lies. 1.1 Graph Theory Background. Informally, a graph is a collection of vertices (that look like dots) and edges (that look like curves), where each edge joins two vertices. Formally, A graph is a pair G =(V,E), where V is a set of dots and E is a set of pairs of vertices. Here are a few examples of graphs, in Figure 1: e b f a Figure 1: Examples of graphs. Note that the word vertex is singular; its plural is vertices. Two vertices that are joined by an edge are called adjacent. For example, the vertices labeled a and b in the leftmost graph of Figure 1 are adjacent. Two edges that meet at a vertex are called incident. For example, the edges labeled e and f in the leftmost graph of Figure 1 are incident. A subgraph is a graph that is contained within another graph. For example, in Figure 1 the second graph is a subgraph of the fourth graph. You can see this at left in Figure 2 where the subgraph in question is emphasized. Figure 2: More examples of graphs. In a connected graph, there is a way to get from any vertex to any other vertex without leaving the graph. The second graph of Figure 2 is not connected. -
A Combinatorial Abstraction of the Shortest Path Problem and Its Relationship to Greedoids
A Combinatorial Abstraction of the Shortest Path Problem and its Relationship to Greedoids by E. Andrew Boyd Technical Report 88-7, May 1988 Abstract A natural generalization of the shortest path problem to arbitrary set systems is presented that captures a number of interesting problems, in cluding the usual graph-theoretic shortest path problem and the problem of finding a minimum weight set on a matroid. Necessary and sufficient conditions for the solution of this problem by the greedy algorithm are then investigated. In particular, it is noted that it is necessary but not sufficient for the underlying combinatorial structure to be a greedoid, and three ex tremely diverse collections of sufficient conditions taken from the greedoid literature are presented. 0.1 Introduction Two fundamental problems in the theory of combinatorial optimization are the shortest path problem and the problem of finding a minimum weight set on a matroid. It has long been recognized that both of these problems are solvable by a greedy algorithm - the shortest path problem by Dijk stra's algorithm [Dijkstra 1959] and the matroid problem by "the" greedy algorithm [Edmonds 1971]. Because these two problems are so fundamental and have such similar solution procedures it is natural to ask if they have a common generalization. The answer to this question not only provides insight into what structural properties make the greedy algorithm work but expands the class of combinatorial optimization problems known to be effi ciently solvable. The present work is related to the broader question of recognizing gen eral conditions under which a greedy algorithm can be used to solve a given combinatorial optimization problem. -
Arxiv:1403.0920V3 [Math.CO] 1 Mar 2019
Matroids, delta-matroids and embedded graphs Carolyn Chuna, Iain Moffattb, Steven D. Noblec,, Ralf Rueckriemend,1 aMathematics Department, United States Naval Academy, Chauvenet Hall, 572C Holloway Road, Annapolis, Maryland 21402-5002, United States of America bDepartment of Mathematics, Royal Holloway University of London, Egham, Surrey, TW20 0EX, United Kingdom cDepartment of Mathematics, Brunel University, Uxbridge, Middlesex, UB8 3PH, United Kingdom d Aschaffenburger Strasse 23, 10779, Berlin Abstract Matroid theory is often thought of as a generalization of graph theory. In this paper we propose an analogous correspondence between embedded graphs and delta-matroids. We show that delta-matroids arise as the natural extension of graphic matroids to the setting of embedded graphs. We show that various basic ribbon graph operations and concepts have delta-matroid analogues, and illus- trate how the connections between embedded graphs and delta-matroids can be exploited. Also, in direct analogy with the fact that the Tutte polynomial is matroidal, we show that several polynomials of embedded graphs from the liter- ature, including the Las Vergnas, Bollab´as-Riordanand Krushkal polynomials, are in fact delta-matroidal. Keywords: matroid, delta-matroid, ribbon graph, quasi-tree, partial dual, topological graph polynomial 2010 MSC: 05B35, 05C10, 05C31, 05C83 1. Overview Matroid theory is often thought of as a generalization of graph theory. Many results in graph theory turn out to be special cases of results in matroid theory. This is beneficial -
Matchgates Revisited
THEORY OF COMPUTING, Volume 10 (7), 2014, pp. 167–197 www.theoryofcomputing.org RESEARCH SURVEY Matchgates Revisited Jin-Yi Cai∗ Aaron Gorenstein Received May 17, 2013; Revised December 17, 2013; Published August 12, 2014 Abstract: We study a collection of concepts and theorems that laid the foundation of matchgate computation. This includes the signature theory of planar matchgates, and the parallel theory of characters of not necessarily planar matchgates. Our aim is to present a unified and, whenever possible, simplified account of this challenging theory. Our results include: (1) A direct proof that the Matchgate Identities (MGI) are necessary and sufficient conditions for matchgate signatures. This proof is self-contained and does not go through the character theory. (2) A proof that the MGI already imply the Parity Condition. (3) A simplified construction of a crossover gadget. This is used in the proof of sufficiency of the MGI for matchgate signatures. This is also used to give a proof of equivalence between the signature theory and the character theory which permits omittable nodes. (4) A direct construction of matchgates realizing all matchgate-realizable symmetric signatures. ACM Classification: F.1.3, F.2.2, G.2.1, G.2.2 AMS Classification: 03D15, 05C70, 68R10 Key words and phrases: complexity theory, matchgates, Pfaffian orientation 1 Introduction Leslie Valiant introduced matchgates in a seminal paper [24]. In that paper he presented a way to encode computation via the Pfaffian and Pfaffian Sum, and showed that a non-trivial, though restricted, fragment of quantum computation can be simulated in classical polynomial time. Underlying this magic is a way to encode certain quantum states by a classical computation of perfect matchings, and to simulate certain ∗Supported by NSF CCF-0914969 and NSF CCF-1217549. -
Matroids You Have Known
26 MATHEMATICS MAGAZINE Matroids You Have Known DAVID L. NEEL Seattle University Seattle, Washington 98122 [email protected] NANCY ANN NEUDAUER Pacific University Forest Grove, Oregon 97116 nancy@pacificu.edu Anyone who has worked with matroids has come away with the conviction that matroids are one of the richest and most useful ideas of our day. —Gian Carlo Rota [10] Why matroids? Have you noticed hidden connections between seemingly unrelated mathematical ideas? Strange that finding roots of polynomials can tell us important things about how to solve certain ordinary differential equations, or that computing a determinant would have anything to do with finding solutions to a linear system of equations. But this is one of the charming features of mathematics—that disparate objects share similar traits. Properties like independence appear in many contexts. Do you find independence everywhere you look? In 1933, three Harvard Junior Fellows unified this recurring theme in mathematics by defining a new mathematical object that they dubbed matroid [4]. Matroids are everywhere, if only we knew how to look. What led those junior-fellows to matroids? The same thing that will lead us: Ma- troids arise from shared behaviors of vector spaces and graphs. We explore this natural motivation for the matroid through two examples and consider how properties of in- dependence surface. We first consider the two matroids arising from these examples, and later introduce three more that are probably less familiar. Delving deeper, we can find matroids in arrangements of hyperplanes, configurations of points, and geometric lattices, if your tastes run in that direction. -
Coded Sparse Matrix Multiplication
Coded Sparse Matrix Multiplication Sinong Wang∗, Jiashang Liu∗ and Ness Shroff∗† ∗Department of Electrical and Computer Engineering yDepartment of Computer Science and Engineering The Ohio State University fwang.7691, liu.3992 [email protected] Abstract r×t In a large-scale and distributed matrix multiplication problem C = A|B, where C 2 R , the coded computation plays an important role to effectively deal with “stragglers” (distributed computations that may get delayed due to few slow or faulty processors). However, existing coded schemes could destroy the significant sparsity that exists in large-scale machine learning problems, and could result in much higher computation overhead, i.e., O(rt) decoding time. In this paper, we develop a new coded computation strategy, we call sparse code, which achieves near optimal recovery threshold, low computation overhead, and linear decoding time O(nnz(C)). We implement our scheme and demonstrate the advantage of the approach over both uncoded and current fastest coded strategies. I. INTRODUCTION In this paper, we consider a distributed matrix multiplication problem, where we aim to compute C = A|B from input matrices A 2 Rs×r and B 2 Rs×t for some integers r; s; t. This problem is the key building block in machine learning and signal processing problems, and has been used in a large variety of application areas including classification, regression, clustering and feature selection problems. Many such applications have large- scale datasets and massive computation tasks, which forces practitioners to adopt distributed computing frameworks such as Hadoop [1] and Spark [2] to increase the learning speed. -
The Geometry of Dimer Models
THE GEOMETRY OF DIMER MODELS DAVID CIMASONI Abstract. This is an expanded version of a three-hour minicourse given at the winterschool Winterbraids IV held in Dijon in February 2014. The aim of these lectures was to present some aspects of the dimer model to a geometri- cally minded audience. We spoke neither of braids nor of knots, but tried to show how several geometrical tools that we know and love (e.g. (co)homology, spin structures, real algebraic curves) can be applied to very natural problems in combinatorics and statistical physics. These lecture notes do not contain any new results, but give a (relatively original) account of the works of Kaste- leyn [14], Cimasoni-Reshetikhin [4] and Kenyon-Okounkov-Sheffield [16]. Contents Foreword 1 1. Introduction 1 2. Dimers and Pfaffians 2 3. Kasteleyn’s theorem 4 4. Homology, quadratic forms and spin structures 7 5. The partition function for general graphs 8 6. Special Harnack curves 11 7. Bipartite graphs on the torus 12 References 15 Foreword These lecture notes were originally not intended to be published, and the lectures were definitely not prepared with this aim in mind. In particular, I would like to arXiv:1409.4631v2 [math-ph] 2 Nov 2015 stress the fact that they do not contain any new results, but only an exposition of well-known results in the field. Also, I do not claim this treatement of the geometry of dimer models to be complete in any way. The reader should rather take these notes as a personal account by the author of some selected chapters where the words geometry and dimer models are not completely irrelevant, chapters chosen and organized in order for the resulting story to be almost self-contained, to have a natural beginning, and a happy ending.