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KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.ORCID iD: 0000-0002-5379-345X
2008 (English)In: Computational Complexity, ISSN 1016-3328, E-ISSN 1420-8954, Vol. 17, no 4, 549-566 p.Article in journal (Refereed) Published
Abstract [en]

We use semidefinite programming to prove that any constraint satisfaction problem in two variables over any domain allows an efficient approximation algorithm that does better than picking a random assignment. Specifically we consider the case when each variable can take values in [d] and that each constraint rejects t out of the d(2) possible input pairs. Then, for some universal constant c, we can, in probabilistic polynomial time, find an assignment whose objective value is, in expectation, within a factor 1 - t/d(2) + ct/d(4) log d of optimal, improving on the trivial bound of 1 - t/d(2).

Place, publisher, year, edition, pages
2008. Vol. 17, no 4, 549-566 p.
Keyword [en]
Semi-definite programming, constraint satisfaction, approximation, algorithms, combinatorial optimization, constraint satisfaction, satisfiability, problems, linear-equations, 2 variables, semidefinite, algorithms, cut
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-18021DOI: 10.1007/s00037-008-0256-yISI: 000261329700006ScopusID: 2-s2.0-57349107952OAI: diva2:336066
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2012-01-23Bibliographically approved

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