Results 21 to 30 of about 6,626 (272)
Energy Reports, 2023 This paper proposes a distributed data-driven distributionally robust volt/var control (DDDR-VVC) approach which schedules on-load-tap changer (OLTC), capacitor banks (CBs) and Photovoltaic (PV) inverters coordinately.
Peishuai Li +4 more
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Distributionally Robust Portfolio Optimization
2019 IEEE 58th Conference on Decision and Control (CDC), 2019 In this paper we consider the problem of portfolio optimization involving uncertainty in the probability distribution of the assets returns. Starting with an estimate of the mean and covariance matrix of the returns of the assets, we define a class of admissible distributions for the returns and show that optimizing the worst-case risk of loss can be ...
I. E. Bardakci, Constantino M. Lagoa
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A distributionally robust perspective on uncertainty quantification and chance constrained programming [PDF]
, 2015 The objective of uncertainty quantification is to certify that a given physical, engineering or economic system satisfies multiple safety conditions with high probability.
Hanasusanto, GA +3 more
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Data-Driven Chance Constrained Optimization under Wasserstein Ambiguity Sets [PDF]
, 2018 We present a data-driven approach for distributionally robust chance constrained optimization problems (DRCCPs). We consider the case where the decision maker has access to a finite number of samples or realizations of the uncertainty.
Cherukuri, Ashish +2 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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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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Robustness to dependency in portfolio optimization using overlapping marginals [PDF]
, 2015 In this paper, we develop a distributionally robust portfolio optimization model where the robustness is across different dependency structures among the random losses.
Doan, Xuan Vinh +2 more
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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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