State-of-the-art mixed integer programming (MIP) solvers are highly parameterized. For heterogeneous and a priori unknown instance distributions, no single parameter configuration generally achieves consistently strong performance, and hence it is useful to select from a portfolio of different solvers. HYDRA is a recent method for using automated algorithm configuration to derive multiple configurations of a single parameterized algorithm for use with portfolio-based selection. This paper shows that, leveraging two key innovations, HYDRA can achieve strong performance for MIP. First, we describe a new algorithm selection approach based on classification with a non-uniform loss function, which significantly improves the performance of algorithm selection for MIP (and SAT). Second, by modifying HYDRA’s method for selecting candidate configurations, we obtain better performance as a function of training time.
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