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- W4380451012 abstract "We propose a supervised learning framework for computing solutions of multi-parametric Mixed Integer Linear Programs (MILPs) that arise in Model Predictive Control. Our approach also quantifies sub-optimality for the computed solutions. Inspired by Branch-and-Bound techniques, the key idea is to train a Neural Network or Random Forest which, for a given parameter, predicts a strategy consisting of (1) a set of Linear Programs (LPs) such that their feasible sets form a partition of the feasible set of the MILP and (2) an integer solution. For control computation and sub-optimality quantification, we solve a set of LPs online in parallel. We demonstrate our approach for a motion planning example and compare against various commercial and open-source mixed-integer programming solvers." @default.
- W4380451012 created "2023-06-14" @default.
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- W4380451012 date "2023-01-01" @default.
- W4380451012 modified "2023-09-27" @default.
- W4380451012 title "Learning for Online Mixed-Integer Model Predictive Control with Parametric Optimality Certificates" @default.
- W4380451012 doi "https://doi.org/10.1109/lcsys.2023.3285778" @default.
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