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Distributionally robust optimization
Acta NumericaDistributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain. A key component of any DRO model is its ambiguity set, that is, a family of probability distributions consistent with any available structural or statistical ...
Daniel Kuhn 0001 +2 more
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The Distributionally Robust Optimization Reformulation for Stochastic Complementarity Problems
Abstract and Applied Analysis, 2014 We investigate the stochastic linear complementarity problem affinely affected by the uncertain parameters. Assuming that we have only limited information about the uncertain parameters, such as the first two moments or the first two moments as well as ...
Liyan Xu, Bo Yu, Wei Liu
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Distributionally Robust Distributed Generation Hosting Capacity Assessment in Distribution Systems
Energies, 2018 Uncertainties associated with the loads and the output power of distributed generations create challenges in quantifying the integration limits of distributed generations in distribution networks, i.e., hosting capacity.
Mohammad Seydali Seyf Abad +3 more
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Energy Reports, 2023 To avoid the problem of insufficient flexibility of the power grid along the cz railway due to source-load fluctuations, a distributionally robust optimization method considering flexibility is proposed in this paper.
Jiawei Liu +5 more
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Distributionally Robust Optimization with Markovian Data
CoRR, 2021 20 ...
Li, Mengmeng +2 more
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Mathematical Foundations of Distributionally Robust Multistage Optimization [PDF]
SIAM Journal on Optimization, 2021 Distributionally robust optimization involves various probability measures in its problem formulation. They can be bundled to constitute a risk functional. For this equivalence, risk functionals constitute a fundamental building block in distributionally robust stochastic programming.
Alois Pichler, Alexander Shapiro 0001
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Relief Network Design Problem: A Distributionally Robust Optimization Approach [PDF]
چشمانداز مدیریت صنعتی, 2021 In this study, a robust two-stage risk-aversion optimization model is proposed for the multi-product relief network design problem. The comprehensive set of decisions for locating and reinforcing relief facilities, inventory planning, and distributing ...
Aliakbar Hasani
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Regularization for Wasserstein distributionally robust optimization
ESAIM: Control, Optimisation and Calculus of Variations, 2023 Optimal transport has recently proved to be a useful tool in various machine learning applications needing comparisons of probability measures. Among these, applications of distributionally robust optimization naturally involve Wasserstein distances in their models of uncertainty, capturing data shifts or worst-case scenarios.
Azizian, Waïss +2 more
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Optimistic Distributionally Robust Policy Optimization
CoRR, 2020 Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), as the widely employed policy based reinforcement learning (RL) methods, are prone to converge to a sub-optimal solution as they limit the policy representation to a particular parametric distribution class.
Jun Song, Chaoyue Zhao
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An Optimal Distributionally Robust Auction
CoRR, 2020 Updated literature review and exposition; results ...
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