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A Simple Approximation Algorithm for the Weighted Matching Problem
 Information Processing Letters
, 2003
"... We present a linear time approximation algorithm with a performance ratio of 1/2 for nding a maximum weight matching in an arbitrary graph. Such a result is already known and is due to Preis [7]. ..."
Abstract

Cited by 30 (3 self)
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We present a linear time approximation algorithm with a performance ratio of 1/2 for nding a maximum weight matching in an arbitrary graph. Such a result is already known and is due to Preis [7].
A lineartime approximation algorithm for weighted matchings in graphs
 ACM TRANSACTIONS ON ALGORITHMS
, 2005
"... Approximation algorithms have so far mainly been studied for problems that are not known to have polynomial time algorithms for solving them exactly. Here we propose an approximation algorithm for the weighted matching problem in graphs which can be solved in polynomial time. The weighted matching p ..."
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Cited by 16 (0 self)
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Approximation algorithms have so far mainly been studied for problems that are not known to have polynomial time algorithms for solving them exactly. Here we propose an approximation algorithm for the weighted matching problem in graphs which can be solved in polynomial time. The weighted matching problem is to find a matching in an edge weighted graph that has maximum weight. The first polynomialtime algorithm for this problem was given by Edmonds in 1965. The fastest known algorithm for the weighted matching problem has a running time of O(nm + n² log n). Many real world problems require graphs of such large size that this running time is too costly. Therefore, there is considerable need for faster approximation algorithms for the weighted matching problem. We present a lineartime approximation algorithm for the weighted matching problem with a performance ratio arbitrarily close to 2/1. This improves the previously best performance ratio of 3/2. Our algorithm is not only of theoretical interest, but because it is easy to implement and the constants involved are quite small it is also useful in practice.
WEIGHTED MATCHING IN THE SEMISTREAMING MODEL
, 2008
"... We reduce the best known approximation ratio for finding a weighted matching of a graph using a onepass semistreaming algorithm from 5.828 to 5.585. The semistreaming model forbids random access to the input and restricts the memory to O(n · polylog n) bits. It was introduced by Muthukrishnan in ..."
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Cited by 8 (0 self)
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We reduce the best known approximation ratio for finding a weighted matching of a graph using a onepass semistreaming algorithm from 5.828 to 5.585. The semistreaming model forbids random access to the input and restricts the memory to O(n · polylog n) bits. It was introduced by Muthukrishnan in 2003 and is appropriate when dealing with massive graphs.
www.stacsconf.org WEIGHTED MATCHING IN THE SEMISTREAMING MODEL
"... Abstract. We reduce the best known approximation ratio for finding a weighted matching of a graph using a onepass semistreaming algorithm from 5.828 to 5.585. The semistreaming model forbids random access to the input and restricts the memory to O(n · polylog n) bits. It was introduced by Muthukr ..."
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Abstract. We reduce the best known approximation ratio for finding a weighted matching of a graph using a onepass semistreaming algorithm from 5.828 to 5.585. The semistreaming model forbids random access to the input and restricts the memory to O(n · polylog n) bits. It was introduced by Muthukrishnan in 2003 and is appropriate when dealing with massive graphs. hal00255934, version 1 14 Feb 2008 1.