Results 21 to 30 of about 68,699 (263)
Adaptive Stochastic Optimization
CoRR, 2020 Optimization lies at the heart of machine learning and signal processing. Contemporary approaches based on the stochastic gradient method are non-adaptive in the sense that their implementation employs prescribed parameter values that need to be tuned for each application.
Frank E. Curtis, Katya Scheinberg
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Journal of Modern Power Systems and Clean Energy, 2017 This paper applies double-uncertainty optimization theory to the operation of AC/DC hybrid microgrids to deal with uncertainties caused by a high proportion of intermittent energy sources.
Peng LI +3 more
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Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
Applied Sciences, 2022 Aiming at the poor effect of traditional denoising algorithms on image enhancement with strong noise, an image denoising algorithm based on improved whale optimization algorithm and parameter adaptive array stochastic resonance is proposed in the paper ...
Weichao Huang +3 more
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Non-Stationary Stochastic Optimization [PDF]
Operations Research, 2013 We consider a non-stationary variant of a sequential stochastic optimization problem, in which the underlying cost functions may change along the horizon. We propose a measure, termed variation budget, that controls the extent of said change, and study how restrictions on this budget impact achievable performance.
Omar Besbes, Yonatan Gur, Assaf Zeevi
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Signal Recovery by Stochastic Optimization [PDF]
Automation and Remote Control, 2019 We discuss an approach to signal recovery in Generalized Linear Models (GLM) in which the signal estimation problem is reduced to the problem of solving a stochastic monotone variational inequality (VI). The solution to the stochastic VI can be found in a computationally efficient way, and in the case when the VI is strongly monotone we derive finite ...
Anatoli B. Juditsky +1 more
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Stochastic-Constrained Stochastic Optimization with Markovian Data
J. Mach. Learn. Res., 2023 This paper considers stochastic-constrained stochastic optimization where the stochastic constraint is to satisfy that the expectation of a random function is below a certain threshold. In particular, we study the setting where data samples are drawn from a Markov chain and thus are not independent and identically distributed.
Yeongjong Kim, Dabeen Lee
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IET Renewable Power Generation, 2021 This paper presents a decision‐driven stochastic adaptive‐robust microgrid operation optimization model considering the uncertainties of wind and solar generations, electricity price, and demand as well as the availability uncertainties of microgrid's ...
Mohammad Reza Ebrahimi, Nima Amjady
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Stochastic Bigger Subspace Algorithms for Nonconvex Stochastic Optimization
IEEE Access, 2021 It is well known that the stochastic optimization problem can be regarded as one of the most hard problems since, in most of the cases, the values of $f$ and its gradient are often not easily to be solved, or the $F(\cdot, \xi)$ is normally not given
Gonglin Yuan +3 more
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A Cooperative Dual to the Nash Equilibrium for Two-Person Prescriptive Games
Journal of Applied Mathematics, 2014 An alternative to the Nash equilibrium (NE) is presented for two-person, one-shot prescriptive games in normal form, where the outcome is determined by an arbiter. The NE is the fundamental solution concept in noncooperative game theory.
H. W. Corley, Phantipa Kwain
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Optimization and Improvement of Gas Spring Design in An Energy Storing Prosthetic Knee
International Journal of Technology, 2015 In this research, an optimization and improvement of gas spring design is discussed. The gas spring is used as a suspension component of an energy storing prosthetic knee. The gas spring replaces the quadricep muscles of transfemoral amputee.
Cucuk Nur Rosyidi +2 more
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