Hybrid search methods synthesize desirable aspects of both constructive and local search methods. Constructive methods are systematic and complete, but exhibit poor performance on large problems because bad decisions made early in the search persist for exponentially long times. In contrast, stochastic local search methods are immune to the tyranny of early mistakes. Local search methods replac...

Combining advantages of different paradigms such as stochastic local search and constraint propagation is a research area of current interest, but implementing such hybrids is a non-trivial task. This paper describes a straightforward combination of existing search and modeling techniques, yielding a local search algorithm that maintains arc-consistency for binary CSPs. On a set of bandwidth gr...

Satisfiability Modulo Theories (SMT) is essential for many practical applications, e.g., in hardand software verification, and increasingly also in other scientific areas like computational biology. A large number of applications in these areas benefit from bit-precise reasoning over finite-domain variables. Current approaches in this area translate a formula over bit-vectors to an equisatisfia...

In the paper we investigate stochastic local search by Markov chain analysis in a high and a low dimensional discrete space. In the n-dimensional space Bn a function called Jump is considered. The analysis shows that an algorithm using a large neighborhood and never accepting worse points performs much better than any local search algorithm accepting worse points with a certain probability. We ...

This paper analyzes the detection of stagnation states in iterated local search algorithms. This is done considering elements such as the population size, the length of the encoding and the number of observed non-improving iterations. This analysis isolates the features of the target problem within one parameter for which three different estimations are given: two static a priori estimations an...

Satisfiability (SAT) problems are a hot topic in the field of combinatorial optimization, having strong theoretical foundations as well as many practical applications. Moreover, approximate SAT algorithms have gained widespread attention because they offer a computationally feasible approach to finding high-quality solutions to NP-hard problems in a scalable and efficient manner. Complete SAT a...