Theory of Nonequilibrium Local Search on Random Satisfaction Problems

Convergence of GENET Guided Local Search for Constraint Satisfaction Problems

On Refutation of Random Constraint Satisfaction Problems

Given a k-ary predicate P , a random instance of a constraint satisfaction problem (CSP) is a set of m constraints on n variables. Each constraint is P applied to k random literals. Such an instance is unsatisfiable with high probability when m n. Although this fact can be proven via a simple probabilistic argument, certifying that a given randomly chosen instance is unsatisfiable, a task calle...

Solving Partial Constraint Satisfaction Problems using Local Search and Abstraction

Partial constraint satisfaction problems PCSPs were proposed by Freuder and Wallace to address some of the representational di culties with traditional constraint satisfaction techniques However the reasoning method of their proposal was limited to traditional backtracking based algorithms In this paper we extend the PCSP model by associating it with a local search algorithm which has found gre...

Stochastic Local Search for Distributed Constraint Satisfaction Problems

Nowadays, many real problems can be solved using local search strategies. These algorithms incrementally alter inconsistency value assignments to all the variables using a repair or hill climbing metaphor to move towards more and more complete solutions. Furthermore, if the problem can be modeled as a distributed problem, the advantages can be even greater. This paper presents a distributed mod...

Partial Constraint Satisfaction Problems and Guided Local Search

A largely unexplored aspect of Constraint Satisfaction Problem (CSP) is that of overconstrained instances for which no solution exists that satisfies all the constraints. In these problems, mentioned in the literature as Partial Constraint Satisfaction Problems (PCSPs), we are often looking for solutions which violate the minimum number of constraints. In more realistic settings, constraints vi...