Simple randomized parallel algorithms for finding a maximal matching in an undirected graph

Author(s):  
S.B. Yang ◽  
S.K. Dhall ◽  
S. Lakshmivarahan
1996 ◽  
Vol 06 (02) ◽  
pp. 213-222 ◽  
Author(s):  
PAOLO FERRAGINA ◽  
FABRIZIO LUCCIO

In this paper we provide three simple techniques to maintain in parallel the minimum spanning tree of an undirected graph under single or batch of edge updates (i.e., insertions and deletions). Our results extend the use of the sparsification data structure to the EREW PRAM model. For proper values of the batch size, our algorithms require less time and work than the best known dynamic parallel algorithms.


1991 ◽  
Vol 01 (02) ◽  
pp. 135-142
Author(s):  
S. B. YANG ◽  
S. K. DHALL ◽  
S. LAKSHMIVARAHAN

In this paper we present a randomized parallel maximal matching algorithm that requires only O(n) processors on an. EREW PRAM, where n is the number of vertices in an undirected graph G = (V, E). Our algorithm outputs a matching in O( log 2 m) expected time, where m is the number of edges in G. The probability that the output of our algorithm is maximal is at least 1 − e−k, where k is a constant.


2021 ◽  
Vol 8 (2) ◽  
pp. 1-20
Author(s):  
Barbara Geissmann ◽  
Lukas Gianinazzi

We present the first near-linear work and poly-logarithmic depth algorithm for computing a minimum cut in an undirected graph. Previous parallel algorithms with poly-logarithmic depth required at least quadratic work in the number of vertices. In a graph with n vertices and m edges, our randomized algorithm computes the minimum cut with high probability in O ( m log 4 n ) work and O (log 3 n ) depth. This result is obtained by parallelizing a data structure that aggregates weights along paths in a tree, in addition exploiting the connection between minimum cuts and approximate maximum packings of spanning trees. In addition, our algorithm improves upon bounds on the number of cache misses incurred to compute a minimum cut.


2016 ◽  
Vol 09 (02) ◽  
pp. 1650019 ◽  
Author(s):  
Zhaocai Wang ◽  
Zuwen Ji ◽  
Ziyi Su ◽  
Xiaoming Wang ◽  
Kai Zhao

The maximal matching problem (MMP) is to find maximal edge subsets in a given undirected graph, that no pair of edges are adjacent in the subsets. It is a vitally important NP-complete problem in graph theory and applied mathematics, having numerous real life applications in optimal combination and linear programming fields. It can be difficultly solved by the electronic computer in exponential level time. Meanwhile in previous studies deoxyribonucleic acid (DNA) molecular operations usually were used to solve NP-complete continuous path search problems, e.g. HPP, traveling salesman problem, rarely for NP-hard problems with discrete vertices or edges solutions, such as the minimum vertex cover problem, graph coloring problem and so on. In this paper, we present a DNA algorithm for solving the MMP with DNA molecular operations. For an undirected graph with [Formula: see text] vertices and [Formula: see text] edges, we reasonably design fixed length DNA strands representing vertices and edges of the graph, take appropriate steps and get the solutions of the MMP in proper length range using [Formula: see text] time. We extend the application of DNA molecular operations and simultaneously simplify the complexity of the computation.


2019 ◽  
Vol 38 (4) ◽  
pp. 817-850 ◽  
Author(s):  
Luisa D'Amore ◽  
Valeria Mele ◽  
Diego Romano ◽  
Giuliano Laccetti

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