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- W2968192014 abstract "In this paper we propose a lightweight neural network architecture that is able to learn the binary components of the optimal solution of a class of multiparametric mixed-integer quadratic programming (MIQP) problems, such as those that arise from hybrid model predictive control formulations. The predictor provides a binary warm-start to a specifically designed branch and bound (B&B) algorithm to quickly discover an integer-feasible solution of the given MIQP, with the aim of reducing the overall solution time required to find the global optimal solution on line." @default.
- W2968192014 created "2019-08-22" @default.
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- W2968192014 creator A5063494900 @default.
- W2968192014 date "2019-06-01" @default.
- W2968192014 modified "2023-10-01" @default.
- W2968192014 title "Learning binary warm starts for multiparametric mixed-integer quadratic programming" @default.
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- W2968192014 doi "https://doi.org/10.23919/ecc.2019.8795808" @default.
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