Computationally Efficient Approximations for Distributionally Robust Optimization Under Moment and Wasserstein Ambiguity
From MaRDI portal
Publication:5087739
DOI10.1287/ijoc.2021.1123OpenAlexW4210665097WikidataQ114058188 ScholiaQ114058188MaRDI QIDQ5087739
Kai Pan, Jianqiang Cheng, Ruiwei Jiang, Meysam Cheramin
Publication date: 1 July 2022
Published in: INFORMS Journal on Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1287/ijoc.2021.1123
stochastic programmingsemidefinite programmingprincipal component analysisWasserstein distancedistributionally robust optimizationmoment information
Related Items (2)
Wasserstein distributionally robust chance-constrained program with moment information ⋮ Bounds for Multistage Mixed-Integer Distributionally Robust Optimization
Uses Software
Cites Work
- Unnamed Item
- New reformulations of distributionally robust shortest path problem
- Distributionally robust single machine scheduling with the total tardiness criterion
- Distributionally robust optimization with matrix moment constraints: Lagrange duality and cutting plane methods
- Data-driven robust optimization
- Ambiguous risk constraints with moment and unimodality information
- Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations
- Robust sample average approximation
- Likelihood robust optimization for data-driven problems
- Solving 0-1 semidefinite programs for distributionally robust allocation of surgery blocks
- Robust empirical optimization is almost the same as mean-variance optimization
- Ambiguous chance constrained problems and robust optimization
- Robust Convex Optimization
- Distributionally Robust Stochastic Knapsack Problem
- Graph Implementations for Nonsmooth Convex Programs
- Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems
- TRACTABLE ROBUST EXPECTED UTILITY AND RISK MODELS FOR PORTFOLIO OPTIMIZATION
- Models for Minimax Stochastic Linear Optimization Problems with Risk Aversion
- Worst-Case Value-At-Risk and Robust Portfolio Optimization: A Conic Programming Approach
- The Price of Robustness
- Lectures on Stochastic Programming
- Asymptotic Statistics
- Distributionally Robust Optimization with Principal Component Analysis
- Semidefinite Programming
- Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls
This page was built for publication: Computationally Efficient Approximations for Distributionally Robust Optimization Under Moment and Wasserstein Ambiguity
