Results 21 to 30 of about 67,878 (263)
A Stochastic Multiobjective Optimization Framework for Wireless Sensor Networks
EURASIP Journal on Wireless Communications and Networking, 2010 In wireless sensor networks (WSNs), there generally exist many different objective functions to be optimized. In this paper, we propose a stochastic multiobjective optimization approach to solve such kind of problem.
Shibo He +5 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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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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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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Distributed delayed stochastic optimization [PDF]
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012 We analyze the convergence of gradient-based optimization algorithms that base their updates on delayed stochastic gradient information. The main application of our results is to the development of gradient-based distributed optimization algorithms where a master node performs parameter updates while worker nodes compute stochastic gradients based on ...
Alekh Agarwal, John C. Duchi
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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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Data-Pooling in Stochastic Optimization [PDF]
SSRN Electronic Journal, 2019 Managing large-scale systems often involves simultaneously solving thousands of unrelated stochastic optimization problems, each with limited data. Intuition suggests that one can decouple these unrelated problems and solve them separately without loss of generality.
Vishal Gupta 0004, Nathan Kallus
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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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