Small maximal matchings of random cubic graphs

We consider the expected size of a smallest maximal matching of cubic graphs. Firstly, we present a randomized greedy algorithm for finding a small maximal matching of cubic graphs. We analyze the average‐case performance of this heuristic on random n‐vertex cubic graphs using differential equations. In this way, we prove that the expected size of the maximal matching returned by the algorithm is asymptotically almost surely (a.a.s.) less than 0.34623n. We also give an existence proof which shows that the size of a smallest maximal matching of a random n‐vertex cubic graph is a.a.s. less than 0.3214n. It is known that the size of a smallest maximal matching of a random n‐vertex cubic graph is a.a.s. larger than 0.3158n. © 2009 Wiley Periodicals, Inc. J Graph Theory 62: 293–323, 2009     

Improved induced matchings in sparse graphs

An induced matching in a graph G=(V,E) is a matching M such that (V,M) is an induced subgraph of G. Clearly, among two vertices with the same neighbourhood (called twins) at most one is matched in any induced matching, and if one of them is matched then there is another matching of the same size that matches the other vertex. Motivated by this, Kanj et al. [10] studied induced matchings in twinless graphs. They showed that any twinless planar graph contains an induced matching of size at least and that there are twinless planar graphs that do not contain an induced matching of size greater than . We improve both these bounds to , which is tight up to an additive constant. This implies that the problem of deciding whether a planar graph has an induced matching of size k has a kernel of size at most 28k. We also show for the first time that this problem is fixed parameter tractable for graphs of bounded arboricity.Kanj et al. also presented an algorithm which decides in -time whether an n-vertex planar graph contains an induced matching of size k. Our results improve the time complexity analysis of their algorithm. However, we also show a more efficient -time algorithm. Its main ingredient is a new, O^∗(4^l)-time algorithm for finding a maximum induced matching in a graph of branch width at most l.     

Recursive matching algorithms and linear orders on the subset lattice

Recursive matching algorithms based on two standard recursions in the subset lattice are analyzed and shown to produce the same explicit matching function ?. It is shown that for a large class of linear orders on the subset lattice the mapping defined recursively by matching in order subsets of size l to first available subsets of size l + 1 is a matching in the lattice and equals ?.     

On a conjecture of Stein

Stein (Pac J Math 59:567–575, 1975) proposed the following conjecture: if the edge set of \(K_{n,n}\) is partitioned into n sets, each of size n, then there is a partial rainbow matching of size \(n-1\). He proved that there is a partial rainbow matching of size \(n(1-\frac{D_n}{n!})\), where \(D_n\) is the number of derangements of [n]. This means that there is a partial rainbow matching of size about \((1- \frac{1}{e})n\). Using a topological version of Hall’s theorem we improve this bound to \(\frac{2}{3}n\).     

The size of 3-uniform hypergraphs with given matching number and codegree

To determine the size of $r$-graphs with given graph parameters is an interesting problem. Chvátal and Hanson (JCTB, 1976) gave a tight upper bound of the size of 2-graphs with restricted maximum degree and matching number; Khare (DM, 2014) studied the same problem for linear 3-graphs with restricted matching number and maximum degree. In this paper, we give a tight upper bound of the size of 3-graphs with bounded codegree and matching number.     

A greedy algorithm for finding a large 2‐matching on a random cubic graph

A 2‐matching of a graph G is a spanning subgraph with maximum degree two. The size of a 2‐matching U is the number of edges in U and this is at least where n is the number of vertices of G and κ denotes the number of components. In this article, we analyze the performance of a greedy algorithm 2greedy for finding a large 2‐matching on a random 3‐regular graph. We prove that with high probability, the algorithm outputs a 2‐matching U with .     

Tree template matching in unranked ordered trees

We consider the problem of tree template matching, a type of tree pattern matching, where the tree templates have some of their leaves denoted as “donʼt care”, and propose a solution based on the bottom-up technique. Specifically, we transform the tree pattern matching problem for unranked ordered trees to a string matching problem, by transforming the tree template and the subject tree to strings representing their postfix bar notation, and then propose a table-driven algorithm to solve it. The proposed algorithm is divided into two phases: the preprocessing and the searching phase. The tree template is preprocessed once, and the searching phase can be applied to many subject trees, without the need of preprocessing the tree template again. Although we prove that the space required for preprocessing is exponential in the size of the tree template in the worst case, we show that for a specific class of tree templates, the space required is linear in the size of the tree template. The time for the searching phase is linear in the size of the subject tree in the worst case. Thus, the algorithm is asymptotically optimal when one needs to search for a given tree template, of constant to logarithmic size, in many subject trees.     

Natural Pseudo-Distance and Optimal Matching between Reduced Size Functions

This paper studies the properties of a new lower bound for the natural pseudo-distance. The natural pseudo-distance is a dissimilarity measure between shapes, where a shape is viewed as a topological space endowed with a real-valued continuous function. Measuring dissimilarity amounts to minimizing the change in the functions due to the application of homeomorphisms between topological spaces, with respect to the L _∞-norm. In order to obtain the lower bound, a suitable metric between size functions, called matching distance, is introduced. It compares size functions by solving an optimal matching problem between countable point sets. The matching distance is shown to be resistant to perturbations, implying that it is always smaller than the natural pseudo-distance. We also prove that the lower bound so obtained is sharp and cannot be improved by any other distance between size functions.     

A las vegas rnc algorithm for maximum matching

Recently two randomized algorithms were discovered that find a maximum matching in an arbitrary graph in polylog time, when run on a parallel random access machine. Both are Monte Carlo algorithms — they have the drawback that with non-zero probability the output is a non-maximum matching. We use the min-max formula for the size of a maximum matching to convert any Monte Carlo maximum matching algorithm into a Las Vegas (error-free) one. The resulting algorithm returns (with high probability) a maximum matching and a certificate proving that the matching is indeed maximum. Research supported by DARPA grant N00039-84-C-0098 and by a US Army Research Office fellowship.     

Existence of rainbow matchings in properly edge-colored graphs

Let G be a properly edge-colored graph. A rainbow matching of G is a matching in which no two edges have the same color. Let δ denote the minimum degree of G. We show that if |V(G)| > (δ ² + 14δ + 1)/4, then G has a rainbow matching of size δ, which answers a question asked by G. Wang [Electron. J. Combin., 2011, 18: #N162] affirmatively. In addition, we prove that if G is a properly colored bipartite graph with bipartition (X, Y) and max{|X|, |Y|} > (δ ² + 4δ − 4)/4, then G has a rainbow matching of size δ.     

Degree Conditions for Matchability in 3‐Partite Hypergraphs

We study conjectures relating degree conditions in 3‐partite hypergraphs to the matching number of the hypergraph, and use topological methods to prove special cases. In particular, we prove a strong version of a theorem of Drisko [14] (as generalized by the first two authors [2]), that every family of $2n-1$ matchings of size n in a bipartite graph has a partial rainbow matching of size n. We show that milder restrictions on the sizes of the matchings suffice. Another result that is strengthened is a theorem of Cameron and Wanless [11], that every $n\times n$ Latin square has a generalized diagonal (set of n entries, each in a different row and column) in which no symbol appears more than twice. We show that the same is true under the weaker condition that the square is row‐Latin.     

Dimension and Matchings in Comparability and Incomparability Graphs

We develop some new inequalities for the dimension of a finite poset. These inequalities are then used to bound dimension in terms of the maximum size of matchings. We prove that if the dimension of P is d and d=3, then there is a matching of size d in the comparability graph of P. There is no analogue of this result for cover graphs, as we show that there is a poset P of dimension d for which the maximum matching in the cover graph of P has size \(O(\log d)\). On the other hand, there is a dual result in which the role of chains and antichains is reversed, as we show that there is also a matching of size d in the incomparability graph of P. The proof of the result for comparability graphs has elements in common with Perles’ proof of Dilworth’s theorem. Either result has the following theorem of Hiraguchi as an immediate corollary: \(\dim (P)\le |P|/2\) when |P|=4.     

Comparison of Multivariate Matching Methods: Structures,Distances, and Algorithms

Abstract

A comparison and evaluation is made of recent proposals for multivariate matched sampling in observational studies, where the following three questions are answered: (1) Algorithms: In current statistical practice, matched samples are formed using “nearest available” matching, a greedy algorithm. Greedy matching does not minimize the total distance within matched pairs, though good algorithms exist for optimal matching that do minimize the total distance. How much better is optimal matching than greedy matching? We find that optimal matching is sometimes noticeably better than greedy matching in the sense of producing closely matched pairs, sometimes only marginally better, but it is no better than greedy matching in the sense of producing balanced matched samples. (2) Structures: In common practice, treated units are matched to one control, called pair matching or 1–1 matching, or treated units are matched to two controls, called 1–2 matching, and so on. It is known, however, that the optimal structure is a full matching in which a treated unit may have one or more controls or a control may have one or more treated units. Optimal 1 — k matching is compared to optimal full matching, finding that optimal full matching is often much better. (3) Distances: Matching involves defining a distance between covariate vectors, and several such distances exist. Three recent proposals are compared. Practical advice is summarized in a final section.     

The matching problem has no small symmetric SDP

Yannakakis (Proceedings of the STOC, pp 223–228, 1988; J Comput Syst Sci 43(3):441–466, 1991. doi: 10.1016/0022-0000(91)90024-Y) showed that the matching problem does not have a small symmetric linear program. Rothvoß (Proceedings of the STOC, pp 263–272, 2014) recently proved that any, not necessarily symmetric, linear program also has exponential size. In light of this, it is natural to ask whether the matching problem can be expressed compactly in a framework such as semidefinite programming (SDP) that is more powerful than linear programming but still allows efficient optimization. We answer this question negatively for symmetric SDPs: any symmetric SDP for the matching problem has exponential size. We also show that an O(k)-round Lasserre SDP relaxation for the asymmetric metric traveling salesperson problem yields at least as good an approximation as any symmetric SDP relaxation of size \(n^{k}\). The key technical ingredient underlying both these results is an upper bound on the degree needed to derive polynomial identities that hold over the space of matchings or traveling salesperson tours.     

Randomized greedy matching. II

We consider the following randomized algorithm for finding a matching M in an arbitrary graph G = (V, E). Repeatedly, choose a random vertex u, then a random neighbour v of u. Add edge {u, v} to M and delete vertices u, v from G along with any vertices that become isolated. Our main result is that there exists a positive constant ? such that the expected ratio of the size of the matching produced to the size of largest matching in G is at least 0.5 + ?. We obtain stronger results for sparse graphs and trees and consider extensions to hypergraphs.     

Unicycle graphs and uniquely restricted maximum matchings

A matching M is called uniquely restricted in a graph G if it is the unique perfect matching of the subgraph induced by the vertices that M saturates. G is a unicycle graph if it owns only one cycle. Golumbic, Hirst and Lewenstein observed that for a tree or a graph with only odd cycles the size of a maximum uniquely restricted matching is equal to the matching number of the graph. In this paper we characterize unicycle graphs enjoying this equality. Moreover, we describe unicycle graphs with only uniquely restricted maximum matchings. Using these findings, we show that unicycle graphs having only uniquely restricted maximum matchings can be recognized in polynomial time.     

Superpolynomial Lower Bounds for Monotone Span Programs

monotone span programs computing explicit functions. The best previous lower bound was by Beimel, Gál, Paterson [7]; our proof exploits a general combinatorial lower bound criterion from that paper. Our lower bounds are based on an analysis of Paley-type bipartite graphs via Weil's character sum estimates. We prove an lower bound for the size of monotone span programs for the clique problem. Our results give the first superpolynomial lower bounds for linear secret sharing schemes. We demonstrate the surprising power of monotone span programs by exhibiting a function computable in this model in linear size while requiring superpolynomial size monotone circuits and exponential size monotone formulae. We also show that the perfect matching function can be computed by polynomial size (non-monotone) span programs over arbitrary fields. Received: August 1, 1996     

A Subquadratic Algorithm for Approximate Regular Expression Matching

The main result of this paper is an algorithm for approximate matching of a regular expression of size m in a text of size n in time O(nm/log_d+2n), where d is the number of allowed errors. This algorithm is the first o(mn) algorithm for approximate matching to regular expressions.     

Text Indexing and Dictionary Matching with One Error

The indexing problem is where a text is preprocessed and subsequent queries of the form “Find all occurrences of pattern P in the text” are answered in time proportional to the length of the query and the number of occurrences. In the dictionary matching problem a set of patterns is preprocessed and subsequent queries of the form “Find all occurrences of dictionary patterns in text T” are answered in time proportional to the length of the text and the number of occurrences.There exist efficient worst-case solutions for the indexing problem and the dictionary matching problem, but none that find approximate occurrences of the patterns, i.e., where the pattern is within a bound edit (or Hamming) distance from the appropriate text location.In this paper we present a uniform deterministic solution to both the indexing and the general dictionary matching problem with one error. We preprocess the data in time O(n log² n), where n is the text size in the indexing problem and the dictionary size in the dictionary matching problem. Our query time for the indexing problem is O(m log n log log n + tocc), where m is the query string size and tocc is the number of occurrences. Our query time for the dictionary matching problem is O(n log³ d log log d + tocc), where n is the text size and d the dictionary size. The time bounds above apply to both bounded and unbounded alphabets.     

BLIM: A New Bit-Parallel Pattern Matching Algorithm Overcoming Computer Word Size Limitation

Bitwise operations are executed very fast in computer architecture. Algorithms aiming to benefit from this intrinsic property can be classified as bit-parallel algorithms. Bit-parallelism has been widely investigated in the pattern matching area since the introduction of the Shift-Or algorithm. In the original idea, there is no shift mechanism, and the input pattern length is required to be less than the computer word size (W) to benefit from the full power of bit-parallelism. The lack of the shift mechanism was removed by the succeeding algorithms of this genre, but W limitation has not been overcome in an elegant way. This study proposes a new bit-parallel algorithm, given name BLIM (bit-parallel length independent matching), for exact pattern matching that does not restrict the input pattern to be shorter than the word size. The multiple pattern case is also addressed, and it is shown that up to computer word size number of patterns, whatever their lengths are, can be searched simultaneously in a single bit-parallel framework. Similar to other algorithms of this genre, BLIM is also capable of handling fixed-length gaps and character classes in the input strings as well. The proposed algorithm is compared with the other alternatives of its class, mainly the shift-or and BNDM variants. Experimental results indicate that BLIM is compatible with the previous bit-parallel algorithms with an additional gain of overcoming the word size limitation.