On Distributionally Robust Multistage Convex Optimization: New Algorithms and Complexity Analysis

Fuente: arXiv
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Autores principales: Zhang, Shixuan, Sun, Xu Andy
Formato: Preprint
Publicado: 2020
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author Zhang, Shixuan
Sun, Xu Andy
author_facet Zhang, Shixuan
Sun, Xu Andy
contents This paper presents an algorithmic study and complexity analysis for solving distributionally robust multistage convex optimization (DR-MCO) problems. Our main contribution is a novel nonconsecutive dual dynamic programming (NDDP) algorithm which explores different stages in an adaptive fashion. In contrast with the usual consecutive dual dynamic programming (CDDP) algorithm, we show that NDDP reduces the subproblem complexity from quadratic to linear dependency on the number of stages. Two different DR-MCO examples are also presented to show the efficiency and effectiveness of the proposed NDDP algorithm.
format Preprint
id arxiv_https___arxiv_org_abs_2010_06759
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle On Distributionally Robust Multistage Convex Optimization: New Algorithms and Complexity Analysis
Zhang, Shixuan
Sun, Xu Andy
Optimization and Control
This paper presents an algorithmic study and complexity analysis for solving distributionally robust multistage convex optimization (DR-MCO) problems. Our main contribution is a novel nonconsecutive dual dynamic programming (NDDP) algorithm which explores different stages in an adaptive fashion. In contrast with the usual consecutive dual dynamic programming (CDDP) algorithm, we show that NDDP reduces the subproblem complexity from quadratic to linear dependency on the number of stages. Two different DR-MCO examples are also presented to show the efficiency and effectiveness of the proposed NDDP algorithm.
title On Distributionally Robust Multistage Convex Optimization: New Algorithms and Complexity Analysis
topic Optimization and Control
url https://arxiv.org/abs/2010.06759