Stochastic local search (SLS) algorithms for the propositional satisfiability problem (SAT) have been successfully applied to solve suitably encoded search problems from various domains. One drawback of these algorithms is that they are usually incomplete. We refine the notion of incompleteness for stochastic decision algorithms by introducing the notion of “probabilistic asymptotic completenes...

Explaining the causes of infeasibility of Boolean formulas has many practical applications in various fields. A small unsatisfiable subformula provides a succinct explanation of infeasibility and is valuable for applications. In recent years the problem of finding unsatisfiable subformulas has been addressed frequently by research works, which are mostly based on the SAT solvers with DPLL backt...

Constraint satisfaction and optimisation is NP-complete by nature. The combinatorial explosion problem prevents complete constraint programming methods from solving many real-life constraint problems. In many situations, stochastic search methods, many of which sacrifice completeness for efficiency, are needed. This paper reports a family of stochastic algorithms for constraint satisfaction and...

We present a new algorithm that uses both local branching and Monte Carlo sampling in a multi-descent search strategy for solving 0-1 integer stochastic programming problems. This procedure is applied to the single vehicle routing problem with stochastic demands. Computational results show the usefulness of this new approach to solve hard instances of the problem.

Stochastic local search methods such as GSAT, WalkSAT and their variants have been used successfully at solving propositional satisfiability problems (SAT). The key operation in these local search algorithms is the speed of variable flipping. We present a parallel FPGA designs for CSAT capable of one flip per clock cycle which is achieved by exploiting maximal parallelism and “multi-try” pipeli...

Corresonding author: Dr. B. Wade Brorsen, Oklahoma State University, Department of Agricultural Economics, 414 Ag Hall, 74078; [email protected], ph. 405-744-6155. Abstract – The training of neural networks is a difficult optimization problem because of the nonconvex objective function. Therefore, as an alternative to local search search algorithms, many global search algorithms have been use...

The genomic median problem aims at finding the organization of the chromosome of a common ancestor to multiple living species. The problem is often formulated as searching for a genome that minimizes some distance measure among given genomes. In our previous research we have proposed a new stochastic local search algorithm for this problem that combined elements of tabu search, iterated local s...

A heuristic algorithm based on VNS is proposed to solve the Max 3-cut and Max 3-section problems. By establishing a neighborhood structure of the Max 3-cut problem, we propose a local search algorithm and a variable neighborhood global search algorithm with two stochastic search steps to obtain the global solution. We give some numerical results and comparisons with the well-known 0.836-approxi...