Flexible Management of Large-Scale Integer Domains in CSPs

Most research on Constraint Programming concerns the (exponential) search space of Constraint Satisfaction Problems (CSPs) and intelligent algorithms that reduce and explore it. This work proposes a different way, not of solving a problem, but of storing the domains of its variables, an important—and less focused—issue especially when they are large. The new data structures that are used are proved theoretically and empirically to adapt better to large domains, than the commonly used ones. The experiments of this work display the contrast between the most popular Constraint Programming systems and a new system that uses the data structures proposed in order to solve CSP instances with wide domains, such as known Bioinformatics problems

Flexible management of large-scale integer domains in CSPs

Most research on Constraint Programming concerns the (exponential) search space of Constraint Satisfaction Problems (CSPs) and intelligent algorithms that reduce and explore it. This work proposes a different way, not of solving a problem, but of storing the domains of its variables, an important - and less focused - issue especially when they are large. The new data structures that are used are proved theoretically and empirically to adapt better to large domains, than the commonly used ones. The experiments of this work display the contrast between the most popular Constraint Programming systems and a new system that uses the data structures proposed in order to solve CSP instances with wide domains, such as known Bioinformatics problems. © Springer-Verlag Berlin Heidelberg 2010

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