A Nonconvex Regularization Scheme for the Stochastic Dual Dynamic Programming Algorithm

Arnab Bhattacharya (), Jeffrey P. Kharoufeh () and Bo Zeng ()
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Arnab Bhattacharya: Pacific Northwest National Laboratory, Richland, Washington 99352
Jeffrey P. Kharoufeh: Department of Industrial Engineering, Clemson University, Clemson, South Carolina 29634
Bo Zeng: Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261

INFORMS Journal on Computing, 2023, vol. 35, issue 5, 1161-1178

Abstract: We propose a new nonconvex regularization scheme to improve the performance of the stochastic dual dynamic programming (SDDP) algorithm for solving large-scale multistage stochastic programs. Specifically, we use a class of nonconvex regularization functions, namely folded concave penalty functions, to improve solution quality and the convergence rate of the SDDP procedure. We develop a strategy based on mixed-integer programming to guarantee global optimality of the nonconvex regularization problem. Moreover, we establish provable convergence guarantees for our customized SDDP algorithm. The benefits of our regularization scheme are demonstrated by solving large-scale instances of two multistage stochastic optimization problems.

Keywords: stochastic programming; nonconvex regularization; SDDP; sampling-based decomposition (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:35:y:2023:i:5:p:1161-1178

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