Finding Solutions By Finding Inconsistencies

In continuous constraint programming, the solving process alternates propagation steps, which reduce the search space according to the constraints, and branching steps. In practice, the solvers spend a lot of computation time in propagation to separate feasible and infeasible parts of the search space. The constraint propagators cut the search space into two subspaces: the inconsistent one, which can be discarded, and the consistent one, which may contain solutions and where the search continues. The status of all this consistent subspace is thus indeterminate. In this article, we introduce a new step called elimination. It refines the analysis of the consistent subspace by dividing it into an indeterminate one, where the search must continue, and a satisfied one, where the constraints are always satisfied. The latter can be stored and removed from the search process. Elimination relies on the propagation of the negation of the constraints, and a new difference operator to efficiently compute the obtained set as an union of boxes, thus it uses the same representations and algorithms as those already existing in the solvers. Combined with propagation, elimination allows the solver to focus on the frontiers of the constraints, which is the core difficult part of the problem. We have implemented our method in the AbSolute solver, and present experimental results on classic benchmarks with good performances.