Using a Skewed Hamming Distance to Speed Up Deterministic Local Search

Details Using a Skewed Hamming Distance to Speed Up Deterministic Local Search Using a Skewed Hamming Distance to Speed Up Deterministic Local Search

2010-05-26

arxiv.org/abs/1005.4874v1

Computer Science

Computational Complexity

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Using a Skewed Hamming Distance to Speed Up Deterministic Local Search does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Using a Skewed Hamming Distance to Speed Up Deterministic Local Search, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Using a Skewed Hamming Distance to Speed Up Deterministic Local Search will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-606695