Abstract:Contemporary research explores the possibilities of integrating machine learning (ML) approaches with traditional combinatorial optimisation solvers. Since optimisation hybrid solvers, which combine propositional satisfiability (SAT) and constraint programming (CP), dominate recent benchmarks, it is surprising that the literature has paid limited attention to machine learning approaches for hybrid CP-SAT solvers. We identify the technique of minimal unsatisfiable subsets as promising to improve the performance… Show more
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