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We propose a methodology, based on machine learning and optimization, forselecting a solver configuration for a given instance. First, we employ a setof solved instances and configurations in order to learn a performance functionof the solver. Secondly, we formulate a mixed-integer nonlinear program wherethe objective/constraints explicitly encode the learnt information, and whichwe solve, upon the arrival of an unknown instance, to find the best solverconfiguration for that instance, based on the performance function. The mainnovelty of our approach lies in the fact that the configuration set searchproblem is formulated as a mathematical program, which allows us to a) enforcehard dependence and compatibility constraints on the configurations, and b)solve it efficiently with off-the-shelf optimization tools.

A learning-based mathematical programming formulation for the automatic configuration of optimization solvers