A 2.5-factor approximation algorithm for the k-MST problem

Arya, Sunil ; Ramesh, H. (1998) A 2.5-factor approximation algorithm for the k-MST problem Information Processing Letters, 65 (3). pp. 117-118. ISSN 0020-0190

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Official URL: http://www.sciencedirect.com/science/article/pii/S...

Related URL: http://dx.doi.org/10.1016/S0020-0190(98)00010-6


The k-MST problem requires finding that subset of at least k vertices of a given graph whose Minimum Spanning Tree has least weight amongst all subsets of at least k vertices. There has been much work on this problem recently, culminating in an approximation algorithm by Garg, which finds a subset of k vertices whose MST has weight at most 3 times the optimal. Garg also argued that a factor of 3 cannot be improved unless lower bounds different from his are used. This argument applies only to the rooted case of the problem. When no root vertex is specified, we show how to use a pruning technique on top of Garg's algorithm to achieve an approximation factor of 2.5. Note that Garg's algorithm is based upon the Goemans-Williamson clustering method, using which it seems hard to obtain any approximation factor better than 2.

Item Type:Article
Source:Copyright of this article belongs to Elsevier Science.
Keywords:Algorithms; Approximation Algorithms; k-MST Problem
ID Code:102371
Deposited On:09 Mar 2018 11:24
Last Modified:09 Mar 2018 11:24

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