On approximations of data-driven chance constrained programs over Wasserstein balls
From MaRDI portal
Publication:6106524
DOI10.1016/j.orl.2023.02.008zbMath1525.90289arXiv2206.00231OpenAlexW4321768970MaRDI QIDQ6106524
Zhi Chen, Wolfram Wiesemann, Daniel Kuhn
Publication date: 3 July 2023
Published in: Operations Research Letters (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2206.00231
Bonferroni's inequalityWasserstein distanceconditional value-at-riskdistributionally robust optimizationambiguous chance constraintsalso-X approximation
Related Items (4)
Emergency medical service location problem based on physical bounds using chance-constrained programming approach ⋮ Distributionally robust joint chance-constrained programming: Wasserstein metric and second-order moment constraints ⋮ Distributionally Favorable Optimization: A Framework for Data-Driven Decision-Making with Endogenous Outliers ⋮ Distributionally robust optimization for sequential decision-making
Cites Work
- Nonanticipative duality, relaxations, and formulations for chance-constrained stochastic programs
- Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations
- On safe tractable approximations of chance constraints
- Distributionally robust joint chance constraints with second-order moment information
- Data-driven distributionally robust chance-constrained optimization with Wasserstein metric
- Energy and reserve dispatch with distributionally robust joint chance constraints
- Distributionally robust chance-constrained programs with right-hand side uncertainty under Wasserstein ambiguity
- Optimized Bonferroni approximations of distributionally robust joint chance constraints
- On distributionally robust chance constrained programs with Wasserstein distance
- A distributionally robust perspective on uncertainty quantification and chance constrained programming
- Chance-constrained set covering with Wasserstein ambiguity
- Adversarial classification via distributional robustness with Wasserstein ambiguity
- Lectures on Modern Convex Optimization
- From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization
- A Robust Optimization Perspective on Stochastic Programming
- ALSO-X and ALSO-X+: Better Convex Approximations for Chance Constrained Programs
- Bicriteria Approximation of Chance-Constrained Covering Problems
- Robust Wasserstein profile inference and applications to machine learning
- Distributionally robust chance constraints for non-linear uncertainties
This page was built for publication: On approximations of data-driven chance constrained programs over Wasserstein balls