文章基本信息

标题：Local Search is Better than Random Assignment for Bounded Occurrence Ordering k-CSPs
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作者：Konstantin Makarychev
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2013
卷号：20
页码：139-147
DOI：10.4230/LIPIcs.STACS.2013.139
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：We prove that the Bounded Occurrence Ordering k-CSP Problem is not approximation resistant. We give a very simple local search algorithm that always performs better than the random assignment algorithm (unless, the number of satisfied constraints does not depend on the ordering). Specifically, the expected value of the solution returned by the algorithm is at least ALG >= AVG + alpha(B,k)(OPT-AVG), where OPT is the value of the optimal solution; AVG is the expected value of the random solution; and alpha(B,k) = Omega_k(B^{-(k+O(1))}) is a parameter depending only on k (the arity of the CSP) and B (the maximum number of times each variable is used in constraints). The question whether bounded occurrence ordering k-CSPs are approximation resistant was raised by Guruswami and Zhou (2012), who recently showed that bounded occurrence 3-CSPs and "monotone" k-CSPs admit a non-trivial approximation.
关键词：approximation algorithms; approximation resistance; ordering CSPs