A hybrid RL and self-supervised learning method accelerates generalized Benders decomposition by 57.5% on a MINLP case study while recovering optimal solutions.
arXiv preprint arXiv:2206.14987 , year=
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A note that flags an oversight in RLT convergence proofs for polynomial optimization and recovers correctness via one extra natural assumption.
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A Hybrid Reinforcement and Self-Supervised Learning Aided Benders Decomposition Algorithm
A hybrid RL and self-supervised learning method accelerates generalized Benders decomposition by 57.5% on a MINLP case study while recovering optimal solutions.
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A note on the convergence guarantees of RLT-based algorithms for polynomial optimization
A note that flags an oversight in RLT convergence proofs for polynomial optimization and recovers correctness via one extra natural assumption.