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Derandomizing approximation algorithms based on semidefinite programming

Mahajan, Sanjeev and Ramesh, H (1996) Derandomizing approximation algorithms based on semidefinite programming. In: SIAM Journal on Computing, 28 (5). pp. 1641-1663.

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Abstract

Remarkable breakthroughs have been made recently in obtaining approximate solutions to some fundamental NP-hard problems, namely Max-Cut, Max k-Cut, Max-Sat, Max-Dicut, Max-bisection, k-vertex coloring, maximum independent set, etc. All these breakthroughs involve polynomial time randomized algorithms based upon semidenite programming, a technique pioneered by Goemans and Williamson. In this paper, we give techniques to derandomize the above class of randomized algorithms, thus obtaining polynomial time deterministic algorithms with the same approximation ratios for the above problems. At the heart of our technique is the use of spherical symmetry to convert a nested sequence of n integrations, which cannot be approximated suciently well in polynomial time, to a nested sequence of just a constant number of integrations, which can be approximated suffciently well in polynomial time.

Item Type: Journal Article
Additional Information: Copyright for this article belongs to Society for Industrial and Applied Mathematics (SIAM)
Keywords: NP-hard;approximation algorithm;derandomization;semidenite programming
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 02 Jun 2004
Last Modified: 19 Sep 2010 04:12
URI: http://eprints.iisc.ernet.in/id/eprint/63

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