Using a Skewed Hamming Distance to Speed Up Deterministic Local Search
Computational Complexity
2010-05-27 v1
Abstract
Schoening presents a simple randomized algorithm for (d,k)-CSP problems with running time (d(k-1)/k)^n poly(n). Here, d is the number of colors, k is the size of the constraints, and n is the number of variables. A derandomized version of this, given by Dantsin et al., achieves a running time of (dk/(k+1))^n poly(n), inferior to Schoening's. We come up with a simple modification of the deterministic algorithm, achieving a running time of (d(k-1)/k * k^d/(k^d-1))^n \poly(n). Though not completely eleminating the gap, this comes very close to the randomized bound for all but very small values of d. Our main idea is to define a graph structure on the set of d colors to speed up local search.
Cite
@article{arxiv.1005.4874,
title = {Using a Skewed Hamming Distance to Speed Up Deterministic Local Search},
author = {Dominik Scheder},
journal= {arXiv preprint arXiv:1005.4874},
year = {2010}
}