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

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

PDF A_2.5_Factor.pdf - Published Version Restricted to Registered users only Download (60kB) | Request a copy

Abstract

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:	Journal Article
Publication:	Information Processing Letters
Publisher:	Elsevier Science
Additional Information:	Copyright of this article belongs to Elsevier Science.
Keywords:	algorithms;approximation algorithms;k-MST problem.
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	07 Jan 2009 12:35
Last Modified:	19 Sep 2010 04:58
URI:	http://eprints.iisc.ac.in/id/eprint/17756

Actions (login required)

View Item