ICNN-enhanced 2SP uses architecturally convex neural networks to enable exact LP embedding of recourse surrogates, replacing MIP formulations and yielding up to 100x speedups on benchmark problems.
Advances in Neural Information Processing Systems, volume 35
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Neural network surrogates approximate expected operational costs in multistage stochastic TEP, delivering near-optimal investment plans with up to 13x faster computation on IEEE test systems.
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.
citing papers explorer
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ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming
ICNN-enhanced 2SP uses architecturally convex neural networks to enable exact LP embedding of recourse surrogates, replacing MIP formulations and yielding up to 100x speedups on benchmark problems.
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Learning a Non-linear Surrogate Model for Multistage Stochastic Transmission Planning
Neural network surrogates approximate expected operational costs in multistage stochastic TEP, delivering near-optimal investment plans with up to 13x faster computation on IEEE test systems.
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Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.