SLS Algorithms for SAT and CSPA discrete

Stochastic local search (SLS) algorithms have been successfully applied to hard combinatorial problems from diierent domains. One important feature of SLS algorithms is the fact that their run-time behavior is characterized by a random variable. Consequently, the detailed knowledge of the run-time distribution provides important information for the analysis of SLS algorithms. In this paper we investigate the empirical run-time distributions for several state-of-the-art stochastic local search algorithms for SAT and CSP. Using statistical analysis techniques, we show that on a variety of problems from both randomized distributions and encodings of the blocks world planning and graph coloring domains, the observed run-time behavior can be characterized by exponential distributions. As a rst direct consequence of this result, we establish that these algorithms can be easily parallelized with optimal speedup.