Abstract
Our goal is to characterize and to be able to predict the search cost, of some of the most important CSP algorithms and heuristics when solving CSP problems by obtaining a statistical model of the algorithm runtime based on inexpensively computed parameters obtained from the CSP problem specification and the associated constraints and nogoods graphs.
Such a model will give us three important items concerning the studied CSP problems. First, the model provides a tool to predict the search cost of a given instance, allowing a portfolio of solvers to decide for the best algorithm before to proceed. Second, the models will give an insight about which are the main features that characterize the complexity of a RBCSP. Finally, another potential benefit of the model is pointing out which features are the algorithms most sensible to, thus helping to guess potential areas of improvement.
Research partially supported by projects TIC2003-00950 and TIN2004-07933-C03-03 funded by the Ministerio de Educación y Ciencia.
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© 2005 Springer-Verlag Berlin Heidelberg
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Béjar, R., Fernández, C., Mateu, C. (2005). Statistical Modelling of CSP Solving Algorithms Performance. In: van Beek, P. (eds) Principles and Practice of Constraint Programming - CP 2005. CP 2005. Lecture Notes in Computer Science, vol 3709. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564751_99
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DOI: https://doi.org/10.1007/11564751_99
Publisher Name: Springer, Berlin, Heidelberg
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