Results 11 to 20 of about 6,180 (210)
Frameworks and Results in Distributionally Robust Optimization
Open Journal of Mathematical Optimization, 2022 The concepts of risk aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. The statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these ...
Rahimian, Hamed, Mehrotra, Sanjay
doaj +3 more sources
Bayesian Distributionally Robust Optimization
SIAM Journal on Optimization, 2023 We introduce a new framework, Bayesian Distributionally Robust Optimization (Bayesian-DRO), for data-driven stochastic optimization where the underlying distribution is unknown. Bayesian-DRO contrasts with most of the existing DRO approaches in the use of Bayesian estimation of the unknown distribution.
Alexander Shapiro, Enlu Zhou, Yifan Lin
openaire +2 more sources
Cooperative Data-Driven Distributionally Robust Optimization [PDF]
IEEE Transactions on Automatic Control, 2020 14 ...
Ashish Cherukuri, Jorge Cortes
openaire +3 more sources
Distributionally Robust Convex Optimization [PDF]
Operations Research, 2014 Distributionally robust optimization is a paradigm for decision making under uncertainty where the uncertain problem data are governed by a probability distribution that is itself subject to uncertainty. The distribution is then assumed to belong to an ambiguity set comprising all distributions that are compatible with the decision maker’s prior ...
Wolfram Wiesemann +2 more
openaire +1 more source
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
openaire +4 more sources
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, C M, Lagoa
openaire +3 more sources
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
core +1 more source
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
core +2 more sources
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
doaj +1 more source
Distributionally Robust Bootstrap Optimization
, 2021 Control architectures and autonomy stacks for complex engineering systems are often divided into layers to decompose a complex problem and solution into distinct, manageable sub-problems. To simplify designs, uncertainties are often ignored across layers, an approach with deep roots in classical notions of separation and certainty equivalence.
Summers, Tyler, Kamgarpour, Maryam
openaire +2 more sources