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We propose a supervised learning framework for computing solutions ofmulti-parametric Mixed Integer Linear Programs (MILPs) that arise in ModelPredictive Control. Our approach also quantifies sub-optimality for thecomputed solutions. Inspired by Branch-and-Bound techniques, the key idea is totrain a Neural Network/Random Forest, which for a given parameter, predicts astrategy consisting of (1) a set of Linear Programs (LPs) such that theirfeasible sets form a partition of the feasible set of the MILP and (2) acandidate integer solution. For control computation and sub-optimalityquantification, we solve a set of LPs online in parallel. We demonstrate ourapproach for a motion planning example and compare against various commercialand open-source mixed-integer programming solvers.

Learning for Online Mixed-Integer Model Predictive Control with Parametric Optimality Certificates