Optimal layer assignment for interconnect
Advances in VLSI and Computer Systems
Data structures for weighted matching and nearest common ancestors with linking
SODA '90 Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
A simple approximation algorithm for the weighted matching problem
Information Processing Letters
A linear-time approximation algorithm for weighted matchings in graphs
ACM Transactions on Algorithms (TALG)
Linear time local improvements for weighted matchings in graphs
WEA'03 Proceedings of the 2nd international conference on Experimental and efficient algorithms
Linear time 1/2 -approximation algorithm for maximum weighted matching in general graphs
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
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The subject of this paper is maximum weight matchings of graphs. An edge set M of a given graph G is called a matching if and only if any pair of edges in M share no endvertices. A maximum weight matching is a matching whose total weight (total sum of edge-weights) is maximum among those of G. The maximum weight matching problem (MWM for short) is to find a maximum weight matching of a given graph. Polynomial algorithms for finding an optimum solution to MWM have already been proposed: for example, an O(|V|4) time algorithm proposed by J. Edmonds, and an O(|E||V|log |V|) time algorithm proposed by H.N. Gabow. Some applications require obtaining a matching of large total weight (not necessarily a maximum one) in realistic computing time. These existing algorithms, however, spend extremely long computing time as the size of a given graph becomes large, and several fast approximation algorithms for MWM have been proposed. In this paper, we propose six approximation algorithms GRS+, GRS_F+, GRS_R+, GRS_S+, LAM_a+ and LAM_as+. They are enhanced from known approximation ones by adding some postprocessings that consist of improved search of weight augmenting paths. Their performance is evaluated through results of computing experiment.