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CONSTRAINTS
2008

A Reinforcement Learning Approach to Interval Constraint Propagation

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A Reinforcement Learning Approach to Interval Constraint Propagation
When solving systems of nonlinear equations with interval constraint methods, it has often been observed that many calls to contracting operators do not participate actively to the reduction of domains of variables. Attempts to statically select a subset of efficient contracting operators fail to offer reliable performance speed-ups. By embedding the recencyweighted average Reinforcement Learning method into a constraint propagation algorithm to dynamically learn the best operators, we show that it is possible to obtain robust algorithms with reliable performances on a range of sparse problems. Using a simple heuristics to compute initial weights, we also achieve significant performance speed-ups for dense problems.
Frédéric Goualard, Christophe Jerman
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2008
Where CONSTRAINTS
Authors Frédéric Goualard, Christophe Jermann
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