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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3 Pith papers cite this work, alongside 11 external citations. Polarity classification is still indexing.
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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.
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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.