Decomposition methods for Wasserstein-based data-driven distributionally robust problems

DOI10.1016/j.orl.2021.07.007OpenAlexW3183234787MaRDI QIDQ2060355

Alexandre Street, Carlos Andrés Gamboa, Tito Homem-de-mello, Davi Michel Valladão

Publication date: 13 December 2021

Published in: Operations Research Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.orl.2021.07.007

zbMATH Keywords

decomposition methodstwo-stage stochastic programmingdistributionally robust optimization

Mathematics Subject Classification ID

Operations research, mathematical programming (90-XX)

Related Items (3)

Distributionally Robust Two-Stage Stochastic Programming ⋮ Stochastic Decomposition Method for Two-Stage Distributionally Robust Linear Optimization ⋮ Solving multistage stochastic linear programming via regularized linear decision rules: an application to hydrothermal dispatch planning

Cites Work

• Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations
• A computational study of a solver system for processing two-stage stochastic LPs with enhanced Benders decomposition
• Data-driven risk-averse stochastic optimization with Wasserstein metric
• Solving two-stage robust optimization problems using a column-and-constraint generation method
• Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network
• A Representation and Economic Interpretation of a Two-Level Programming Problem
• Decomposition Algorithms for Two-Stage Distributionally Robust Mixed Binary Programs
• Distributionally Robust Stochastic Dual Dynamic Programming
• JuMP: A Modeling Language for Mathematical Optimization

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