Finding Perfect Matchings Research Articles

A Faster Algorithm for Cuckoo Insertion and Bipartite Matching in Large Graphs

Hash tables are ubiquitous in computer science for efficient access to large datasets. However, there is always a need for approaches that offer compact memory utilisation without substantial degradation of lookup performance. Cuckoo hashing is an efficient technique of creating hash tables with high space utilisation and offer a guaranteed constant access time. We are given n locations and m items. Each item has to be placed in one of the $$k\ge 2$$ locations chosen by k random hash functions. By allowing more than one choice for a single item, cuckoo hashing resembles multiple choice allocations schemes. In addition it supports dynamically changing the location of an item among its possible locations. We propose and analyse an insertion algorithm for cuckoo hashing that runs in linear time with high probability and in expectation. Previous work on total allocation time has analysed breadth first search, and it was shown to be linear only in expectation. Our algorithm finds an assignment (with probability 1) whenever it exists. In contrast, the other known insertion method, known as random walk insertion, may run indefinitely even for a solvable instance. We also present experimental results comparing the performance of our algorithm with the random walk method, also for the case when each location can hold more than one item. As a corollary we obtain a linear time algorithm (with high probability and in expectation) for finding perfect matchings in a special class of sparse random bipartite graphs. We support this by performing experiments on a real world large dataset for finding maximum matchings in general large bipartite graphs. We report an order of magnitude improvement in the running time as compared to the Hopkraft–Karp matching algorithm.

Even Delta-Matroids and the Complexity of Planar Boolean CSPs

The main result of this article is a generalization of the classical blossom algorithm for finding perfect matchings. Our algorithm can efficiently solve Boolean CSPs where each variable appears in exactly two constraints (we call it edge CSP) and all constraints are even Δ-matroid relations (represented by lists of tuples). As a consequence of this, we settle the complexity classification of planar Boolean CSPs started by Dvořák and Kupec. Using a reduction to even Δ-matroids, we then extend the tractability result to larger classes of Δ-matroids that we call efficiently coverable . It properly includes classes that were known to be tractable before, namely, co-independent , compact , local , linear , and binary , with the following caveat: We represent Δ-matroids by lists of tuples, while the last two use a representation by matrices. Since an n × n matrix can represent exponentially many tuples, our tractability result is not strictly stronger than the known algorithm for linear and binary Δ-matroids.

Foundations of Boij–Söderberg theory for Grassmannians

Boij–Söderberg theory characterizes syzygies of graded modules and sheaves on projective space. This paper continues earlier work with Sam, extending the theory to the setting of $\text{GL}_{k}$-equivariant modules and sheaves on Grassmannians. Algebraically, we study modules over a polynomial ring in $kn$ variables, thought of as the entries of a $k\times n$ matrix. We give equivariant analogs of two important features of the ordinary theory: the Herzog–Kühl equations and the pairing between Betti and cohomology tables. As a necessary step, we also extend previous results, concerning the base case of square matrices, to cover complexes other than free resolutions. Our statements specialize to those of ordinary Boij–Söderberg theory when $k=1$. Our proof of the equivariant pairing gives a new proof in the graded setting: it relies on finding perfect matchings on certain graphs associated to Betti tables and to spectral sequences. As an application, we construct three families of extremal rays on the Betti cone for $2\times 3$ matrices.

Tight bounds and conjectures for the Isolation Lemma

Given a hypergraph $H$ and a weight function $w: V \rightarrow \{1, \dots, M\}$ on its vertices, we say that $w$ is isolating if there is exactly one edge of minimum weight $w(e) = \sum_{i \in e} w(i)$. The Isolation Lemma is a combinatorial principle introduced in Mulmuley et. al (1987) which gives a lower bound on the number of isolating weight functions. Mulmuley used this as the basis of a parallel algorithm for finding perfect graph matchings. It has a number of other applications to parallel algorithms and to reductions of general search problems to unique search problems (in which there are one or zero solutions). The original bound given by Mulmuley et al. was recently improved by Ta-Shma (2015). In this paper, we show improved lower bounds on the number of isolating weight functions, and we conjecture that the extremal case is when $H$ consists of $n$ singleton edges. When $M \gg n$ our improved bound matches this extremal case asymptotically. We are able to show that this conjecture holds in a number of special cases: when $H$ is a linear hypergraph or is 1-degenerate, or when $M = 2$. We also show that it holds asymptotically when $M \gg n \gg 1$.

Finding Perfect Matchings in Bipartite Hypergraphs

Haxell's condition [16] is a natural hypergraph analog of Hall's condition, which is a well-known necessary and sufficient condition for a bipartite graph to admit a perfect matching. That is, when Haxell's condition holds it forces the existence of a perfect matching in the bipartite hypergraph. Unlike in graphs, however, there is no known polynomial time algorithm to find the hypergraph perfect matching that is guaranteed to exist when Haxell's condition is satisfied.We prove the existence of an efficient algorithm to find perfect matchings in bipartite hypergraphs whenever a stronger version of Haxell's condition holds. Our algorithm can be seen as a generalization of the classical Hungarian algorithm for finding perfect matchings in bipartite graphs. The techniques we use to achieve this result could be of use more generally in other combinatorial problems on hypergraphs where disjointness structure is crucial, e.g. Set Packing.

Santa claus meets hypergraph matchings

We consider the restricted assignment version of the problem of max-min fair allocation of indivisible goods, also known as the Santa Claus problem . There are m items and n players. Every item has some nonnegative value, and every player is interested in only some of the items. The goal is to distribute the items to the players in a way that maximizes the minimum of the sum of the values of the items given to any player. It was previously shown via a nonconstructive proof that uses the Lovász local lemma that the integrality gap of a certain configuration LP for the problem is no worse than some (unspecified) constant. This gives a polynomial-time algorithm to estimate the optimum value of the problem within a constant factor, but does not provide a polynomial-time algorithm for finding a corresponding allocation. We use a different approach to analyze the integrality gap. Our approach is based upon local search techniques for finding perfect matchings in certain classes of hypergraphs. As a result, we prove that the integrality gap of the configuration LP is no worse than 1/4. Our proof provides a local search algorithm which finds the corresponding allocation, but is nonconstructive in the sense that this algorithm is not known to converge to a local optimum in a polynomial number of steps.

Minimizing the Stabbing Number of Matchings, Trees, and Triangulations

The (axis-parallel) stabbing number of a given set of line segments is the maximum number of segments that can be intersected by any one (axis-parallel) line. This paper deals with finding perfect matchings, spanning trees, or triangulations of minimum stabbing number for a given set of vertices. The complexity of finding a spanning tree of minimum stabbing number is one of the original 30 questions on “The Open Problems Project” list of outstanding problems in computational geometry by Demaine, Mitchell, and O’Rourke. We show $\mathcal{N}\mathcal{P}$-hardness of stabbing problems by means of a general proof technique. For matchings, this also implies a nontrivial lower bound on the approximability. On the positive side, we propose a cut-based integer programming formulation for minimizing the stabbing number of matchings and spanning trees. From the corresponding linear programming relaxation we obtain polynomial-time lower bounds and show that there always is an optimal fractional solution that contains an edge of at least constant weight. We conjecture that the resulting iterated rounding scheme constitutes a constant-factor approximation algorithm.

Processor Efficient Parallel Matching

In this paper we consider the problem of finding perfect matchings in parallel. We present a RNC algorithm with almost optimal work with respect to sequential algorithms, i.e., it uses O(n ω ) processors, where ω is the matrix multiplication exponent. Our algorithm is based on an RNC algorithm for computing determinant of a degree one polynomial matrix which is of independent interest.

Packing of twinned circles on a sphere

The twinned-circle problem is to pack 2 N non-overlapping equal circles forming N pairs of twins (rigidly connected neighbours) on a sphere so that the angular radius of the circles will be as large as possible. In the case that the contact graph(s) of the unconstrained circle packing support(s) at least one perfect matching, a complete solution to the twinned circles problem is found, with the same angular radius as the unconstrained problem. Solutions for N =2–12 pairs of twins are counted and classified by symmetry. For N =2–6 and 12, these are mathematically proven to be the best solutions; for N =7–11, they are based on the best known conjectured solutions of the unconstrained problem. Where the contact graph of the unconstrained problem has one or more rattling circles, the twinned problem is most easily solved by finding perfect matchings of an augmented graph in which each rattling circle is supposed to be simultaneously in contact with all its contactable neighbours. The underlying contact graphs for the unconstrained packings for N =2–12 are all Hamiltonian, guaranteeing the existence of perfect matchings, but Hamiltonicity is not a necessary condition: the first solution to the twins problem based on an example of a non-Hamiltonian contact graph occurs at N =16.

Efficient Algorithms for Petersen's Matching Theorem

Petersen's theorem is a classic result in matching theory from 1891, stating that every 3-regular bridgeless graph has a perfect matching. Our work explores efficient algorithms for finding perfect matchings in such graphs. Previously, the only relevant matching algorithms were for general graphs, and the fastest algorithm ran in O(n3/2) time for 3-regular graphs. We have developed an O(nlog4n)-time algorithm for perfect matching in a 3-regular bridgeless graph. When the graph is also planar, we have as the main result of our paper an optimal O(n)-time algorithm. We present three applications of this result: terrain guarding, adaptive mesh refinement, and quadrangulation.

Random parallel algorithms for finding exact branchings, perfect matchings, and cycles

In this paper we devise randomized parallel algorithms which given a unary weighted (di)graphG=(V, E)construct in time O(log2¦ V¦) branchings, perfect matchings, and disjoint cycles of weightexactly k belonging toG. These problems have been studied by Papadimitriou and Yannakakis [PY1], by Barahona and Pulleyblank [BP], by Cameriniet al [CGM], and by Mulmuleyet al. [MVV]. Our algorithms improve previous solutions. Moreover, we give an NC2 algorithm for computing the absolute value of the pfaffian of a skew-symmetric matrix.

Routing permutations on a graph

AbstractWe study off‐line routing of permutations π in arbitrary connected graphs. By insisting that routings be congestion‐free, we can rephrase the problem as one of factoring π into a minimal length product of permutations that move messages a distance of at most one, or alternatively, as the problem of finding a shortest path from the identity permutation to π in a certain Cayley graph of the symmetric group on the nodes. For the complete k‐partite graph Kn,n,….,n, every π can be routed in two steps. We investigate a method for finding minimal factorizations. It involves finding perfect matchings in certain bipartite graphs associated with π. For the n‐dimensional hypercube Qn, the well‐known upper bound of 2n − 1 for the number of steps in which all π′s can be routed (obtained via the Beneš network) is reduced to 2n − 2. We prove some results about permutations of Qn that are close to the antipodal map. A number of examples on Q3 and Q4 illustrate our method and compare it with those used by others. © 1993 by John Wiley & Sons, Inc.