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arXiv · 2112.00874

Neural Stochastic Dual Dynamic Programming

Hanjun Dai·Yuan Xue·Zia Syed·Dale Schuurmans·Bo Dai
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Abstract

Stochastic dual dynamic programming (SDDP) is a state-of-the-art method for solving multi-stage stochastic optimization, widely used for modeling real-world process optimization tasks. Unfortunately, SDDP has a worst-case complexity that scales exponentially in the number of decision variables, which severely limits applicability to only low dimensional problems. To overcome this limitation, we extend SDDP by introducing a trainable neural model that learns to map problem instances to a piece-wise linear value function within intrinsic low-dimension space, which is architected specifically to interact with a base SDDP solver, so that can accelerate optimization performance on new instances. The proposed Neural Stochastic Dual Dynamic Programming ($\nu$-SDDP) continually self-improves by solving successive problems. An empirical investigation demonstrates that $\nu$-SDDP can significantly reduce problem solving cost without sacrificing solution quality over competitors such as SDDP and reinforcement learning algorithms, across a range of synthetic and real-world process optimization problems.

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Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai. 2021-12-01. Neural Stochastic Dual Dynamic Programming. https://arxiv.org/abs/2112.00874

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