Data-driven distributionally robust risk-averse two-stage stochastic linear programming over Wasserstein ball
From MaRDI portal
Publication:6142068
DOI10.1007/s10957-023-02331-zOpenAlexW4389398637MaRDI QIDQ6142068
Yining Gu, Yi-Cheng Huang, Yan-Jun Wang
Publication date: 25 January 2024
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10957-023-02331-z
Wasserstein metricconic optimizationconditional value-at-riskdistributionally robust optimizationtwo-stage stochastic linear programmingdata-driven decision making
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Two-stage stochastic linear programs with incomplete information on uncertainty
- On general minimax theorems
- Maxmin expected utility with non-unique prior
- Robust two-stage stochastic linear optimization with risk aversion
- Risk-averse two-stage stochastic programming with an application to disaster management
- Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations
- $K$-adaptability in two-stage distributionally robust binary programming
- Robust optimization-methodology and applications
- Distributionally robust joint chance constraints with second-order moment information
- Robust tracking error portfolio selection with worst-case downside risk measures
- Quantitative stability analysis for minimax distributionally robust risk optimization
- A model of distributionally robust two-stage stochastic convex programming with linear recourse
- Conditional value-at-risk in stochastic programs with mixed-integer recourse
- Tractable reformulations of two-stage distributionally robust linear programs over the type-\(\infty\) Wasserstein ball
- Coherent Measures of Risk
- Distributionally Robust Convex Optimization
- Risk-Averse Two-Stage Stochastic Linear Programming: Modeling and Decomposition
- Expected Utility, Penalty Functions, and Duality in Stochastic Nonlinear Programming
- Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems
- Distributionally Robust Optimization and Its Tractable Approximations
- From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization
- Models for Minimax Stochastic Linear Optimization Problems with Risk Aversion
- A Linear Decision-Based Approximation Approach to Stochastic Programming
- A Variable-Complexity Norm Maximization Problem
- Introduction to Stochastic Programming
- Minimax analysis of stochastic problems
- Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls
- Quantifying Distributional Model Risk via Optimal Transport
- Robust Wasserstein profile inference and applications to machine learning
- Sharing the value‐at‐risk under distributional ambiguity
