Constraint satisfaction as global optimization
We present an optimization formulation for discrete binary CSP, based on the construction of a continuous function A(P) whose global maximum represents the best possible solution for that problem. By the best possible solution we mean either (i) a solution of the problem, if it is solvable, or (ii)...
| Autores: | , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 1995 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/96820 |
| Acceso en línea: | https://hdl.handle.net/2117/96820 |
| Access Level: | acceso abierto |
| Palabra clave: | Discrete binary CSP Constraint satisfaction Global optimization Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica |
| Sumario: | We present an optimization formulation for discrete binary CSP, based on the construction of a continuous function A(P) whose global maximum represents the best possible solution for that problem. By the best possible solution we mean either (i) a solution of the problem, if it is solvable, or (ii) a partial solution violating a minimal number of constraints, if the problem is unsolvable. This approach is based on relaxation labeling techniques used to enforce consistency in image interpretation. We have used a projected gradient ascent algorithm to maximize A(P) on the n-queens problem obtaining good results but with a high computational costs. To elude this problem, we have developed a heuristic for variable and value selection inspired in the direction in which A(P) is maximized. We have tested this heuristic with forward checking on several classes of CSP. |
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