On-Demand Bound Computation for Best-First Constraint Optimization

An important class of algorithms for constraint optimization searches for solutions guided by a heuristic evaluation function (bound). When only a few best solutions are required, significant effort can be wasted pre-computing bounds that are not used during the search. We introduce a method that generates - based on lazy, best-first variants of constraint projection and combination operators - only those bounds that are specifically required in order to generate a next best solution.

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