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On level regularization with normal solutions in decomposition methods for multistage stochastic programming problems

Wim van Ackooij (wim.van.ackooij***at***gmail.com)
Welington de Oliveira (welingtonluis***at***gmail.com)
Yongjia Song (ysong3***at***vcu.edu)

Abstract: We consider well-known decomposition techniques for multistage stochastic programming and a new scheme based on normal solutions for stabilizing iterates during the solution process. The given algorithms combine ideas from finite perturbation of convex programs and level bundle methods to regularize the so-called forward step of these decomposition methods. Numerical experiments on a hydrothermal scheduling problem indicate that our algorithms are competitive with the state-of-the-art approaches such as multistage regularized decomposition and stochastic dual dynamic programming.

Keywords: Normal Solution, SDDP algorithm, Stochastic Optimization, Nonsmooth optimization

Category 1: Stochastic Programming

Category 2: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Citation: submitted paper

Download: [PDF]

Entry Submitted: 01/10/2017
Entry Accepted: 01/10/2017
Entry Last Modified: 12/06/2017

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