SIAM Journal on Optimization· 2026Q1
On Distributionally Robust Multistage Convex Optimization: New Algorithms and Complexity Analysis
- 2citations
- Q1SCImago
- 2026year
Short summary
A new nonconsecutive dual dynamic programming (NDDP) algorithm solves distributionally robust multistage convex optimization (DR-MCO) problems by reducing subproblem complexity from quadratic to linear dependency on the number of stages.
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Key points
- Introduces a novel nonconsecutive dual dynamic programming (NDDP) algorithm for DR-MCO.
- NDDP reduces subproblem complexity from O(T^2) to O(T), where T is the number of stages.
- The algorithm explores stages adaptively, differing from standard consecutive approaches.
- Two DR-MCO examples validate the algorithm's efficiency and effectiveness.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract. 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 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.
The authors' abstract, as published at the source. SIAM Journal on Optimization, 2026 · DOI ↗
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Field: Management Science and Operations Research
Management Science and Operations ResearchDecision Sciences