Results 71 to 80 of about 8,860,352 (207)
Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization
, 2018 We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions. The objective is to determine power schedules for controllable devices in a power network to balance ...
camacho +8 more
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Nonlinear distributionally robust optimization
Mathematical ProgrammingThis article focuses on a class of distributionally robust optimization (DRO) problems where, unlike the growing body of the literature, the objective function is potentially nonlinear in the distribution. Existing methods to optimize nonlinear functions in probability space use the Frechet derivatives, which present theoretical and computational ...
Mohammed Rayyan Sheriff +1 more
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DronesThis paper addresses the cooperative task assignment problem for heterogeneous unmanned aerial vehicles with time windows considering uncertain fuel consumption. In the scenario where probabilistic fuel consumption exists and its distribution needs to be
Zhichao Gao +4 more
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Distributionally Robust Transfer Learning
, 2023 Many existing transfer learning methods rely on leveraging information from source data that closely resembles the target data. However, this approach often overlooks valuable knowledge that may be present in different yet potentially related auxiliary samples.
Xiong, Xin, Guo, Zijian, Cai, Tianxi
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Mathematics, 2022 Empirical evidence suggests that financial risk has a heavy-tailed profile. Motivated by recent advances in the generalized quantile risk measure, we propose the tail value-at-risk (TVaR)-based expectile, which can capture the tail risk compared with the
Haoyu Chen, Kun Fan
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Future InternetEarthquake disasters often cause communication base stations to fail, severely hindering rescue operations and information transmission. While traditional air-ground collaborative emergency communication systems can rapidly restore communications, they ...
Miao Miao, Wei Wang, Xiaokai Lian
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Learning Models with Uniform Performance via Distributionally Robust Optimization [PDF]
arXiv.org, 2018 A common goal in statistics and machine learning is to learn models that can perform well against distributional shifts, such as latent heterogeneous subpopulations, unknown covariate shifts, or unmodeled temporal effects.
John C. Duchi, Hongseok Namkoong
semanticscholar +1 more source
EnergiesAiming at multi-agent interest demands and environmental benefits, a distributionally robust game-theoretic optimization algorithm based on a green certificate–carbon trading mechanism is proposed for uncertain microgrids. At first, correlated wind–solar
Chen Wei +4 more
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IEEE Access, 2021 Since optimal portfolio strategy depends heavily on the distribution of uncertain returns, this article proposes a new method for the portfolio optimization problem with respect to distribution uncertainty.
Ningning Du, Yankui Liu, Ying Liu
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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
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