Abstract
We investigate cost propagation for solving combinatorial optimization problems with finite domain variables, expressed as an additive component model. Cost propagation combines ideas from both constraint programming and integer programming into a single approach, where problems are iteratively solved by numerical propagation, with updates for single constraint terms in the component model.
We outline a theory of propagation in terms of equivalent problems with notions of consistency, local optimality, convergence and bounds. We define several different updates and analyze their properties.
Finally, we outline computational experiments on the simple assignment problem, the set partitioning problem, and a crossword puzzle. The experiments illustrate that even without a top level search, cost propagation can by itself solve non-trivial problems, and also be attractive compared to standard methods.
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Grohe, B., Wedelin, D. (2008). Cost Propagation – Numerical Propagation for Optimization Problems. In: Perron, L., Trick, M.A. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2008. Lecture Notes in Computer Science, vol 5015. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68155-7_10
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DOI: https://doi.org/10.1007/978-3-540-68155-7_10
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