基于半定规划松弛层次的逼近算法

E. Chlamtáč
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引用次数: 89

摘要

我们。在组合问题近似算法的背景下,介绍了一种基于Lasserre层次结构的半定规划(SOP)松弛研究框架。作为我们方法的应用,我们给出了两个问题的改进近似算法。我们证明了对于某个固定常数epsiv > 0,给定一个3-一致超图,它包含一个大小为(1/2 - epsiv)v的独立集,我们可以找到一个大小为Omega(nesiv)的独立集。这改进了Krivelevich, Nathaniel和Sitdakov的结果,他们给出了一个算法,为具有独立大小集gamman的超图找到大小为Omega(n6gamma-3)的独立集(但不能保证gamma 1/2)。在Aurora、Clamtac和Charikar的基础上,提出了一种针对3色图求0 (n0.2072)色的算法。我们的方法与其他问题的Lovasz Schrijver线性规划(LP)和SDP层次结构中的一系列不可近似结果形成鲜明对比。
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Approximation Algorithms Using Hierarchies of Semidefinite Programming Relaxations
We. introduce, a framework for studying semidefiniie programming (SOP) relaxations based on the Lasserre hierarchy in the context of approximation algorithms for combinatorial problems. As an application of our approach, we give, improved approximation algorithms for two problems. We show that for some fixed constant epsiv > 0, given a 3-uniform hypergraph containing an independent set of size (1/2 - epsiv)v, we can find an independent set of size Omega(nepsiv). This improves upon the result of Krivelevich, Nathaniel and Sitdakov, who gave an algorithm finding an independent set of size Omega(n6gamma-3) for hypergraphs with an independent set of size gamman (but no guarantee for gamma les 1/2). We also give an algorithm which finds an O(n0.2072)-coloring given a 3-colorable graph, improving upon the work of Aurora, Clamtac and Charikar. Our approach stands in contrast to a long series of inapproximability results in the Lovasz Schrijver linear programming (LP) and SDP hierarchies for other problems.
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