Relationship between clustering and algorithmic phase transitions in the random k-XORSAT model and its NP-complete extensions

Computer Science – Computational Complexity

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

15 pages, 4 figures, Proceedings of the International Workshop on Statistical-Mechanical Informatics, September 16-19, 2007, K

Scientific paper

10.1088/1742-6596/95/1/012013

We study the performances of stochastic heuristic search algorithms on Uniquely Extendible Constraint Satisfaction Problems with random inputs. We show that, for any heuristic preserving the Poissonian nature of the underlying instance, the (heuristic-dependent) largest ratio $\alpha_a$ of constraints per variables for which a search algorithm is likely to find solutions is smaller than the critical ratio $\alpha_d$ above which solutions are clustered and highly correlated. In addition we show that the clustering ratio can be reached when the number k of variables per constraints goes to infinity by the so-called Generalized Unit Clause heuristic.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Relationship between clustering and algorithmic phase transitions in the random k-XORSAT model and its NP-complete extensions 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 Relationship between clustering and algorithmic phase transitions in the random k-XORSAT model and its NP-complete extensions, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Relationship between clustering and algorithmic phase transitions in the random k-XORSAT model and its NP-complete extensions will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-414305

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.