Constraint satisfaction over a non-boolean domain: Approximation algorithms and unique-games hardness

22Citations
Citations of this article
9Readers
Mendeley users who have this article in their library.
Get full text

Abstract

We study the approximability of the MAX k-CSP problem over non-boolean domains, more specifically over {0,1,...,q - 1} for some integer q. We extend the techniques of Samorodnitsky and Trevisan [19] to obtain a UGC hardness result when q is a prime. More precisely, assuming the Unique Games Conjecture, we show that it is NP-hard to approximate the problem to a ratio greater than q 2 k/q k . Independent of this work, Austrin and Mossel [2] obtain a more general UGC hardness result using entirely different techniques. We also obtain an approximation algorithm that achieves a ratio of C(q) •k/q k for some constant C(q) depending only on q, via a subroutine for approximating the value of a semidefinite quadratic form when the variables take values on the corners of the q-dimensional simplex. This generalizes an algorithm of Nesterov [16] for the ±1-valued variables. It has been pointed out to us [15] that a similar approximation ratio can be obtained by reducing the non-boolean case to a boolean CSP. © 2008 Springer-Verlag Berlin Heidelberg.

Cite

CITATION STYLE

APA

Guruswami, V., & Raghavendra, P. (2008). Constraint satisfaction over a non-boolean domain: Approximation algorithms and unique-games hardness. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5171 LNCS, pp. 77–90). https://doi.org/10.1007/978-3-540-85363-3_7

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free