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On the advantage over a random assignment
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0002-5379-345X
2004 (English)In: Random structures & algorithms (Print), ISSN 1042-9832, E-ISSN 1098-2418, Vol. 25, no 2, 117-149 p.Article in journal (Refereed) Published
Abstract [en]

We initiate the study of a new measure of approximation. This measure compares the performance of an approximation algorithm to the random assignment algorithm. This is a useful measure for optimization problems where the random assignment algorithm is known to give essentially the best possible polynomial time approximation. In this paper, we focus on this measure for the optimization problems Max-Lin-2 in which we need to maximize the number of satisfied linear equations in a system of linear equations modulo 2, and Max-k-Lin-2, a special case of the above problem in which each equation has at most k variables. The main techniques we use, in our approximation algorithms and inapproximability results for this measure, are from Fourier analysis and derandomization.

Place, publisher, year, edition, pages
2004. Vol. 25, no 2, 117-149 p.
Keyword [en]
linear system of equations, inapproximability, PCP, approximation algorithms, satisfiability
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-23654DOI: 10.1002/rsa.20031ISI: 000223363800001ScopusID: 2-s2.0-11144258880OAI: diva2:342353
QC 20100525 QC 20110923Available from: 2010-08-10 Created: 2010-08-10 Last updated: 2012-01-21Bibliographically approved

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Håstad, Johan
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