Results 31 to 40 of about 2,320,712 (255)
On the Convergence of Stochastic Process Convergence Proofs
Mathematics, 2021 Convergence of a stochastic process is an intrinsic property quite relevant for its successful practical for example for the function optimization problem.
Borja Sánchez-López, Jesus Cerquides
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Optimization with multivariate conditional value-at-risk constraints [PDF]
, 2013 For many decision making problems under uncertainty, it is crucial to develop risk-averse models and specify the decision makers' risk preferences based on multiple stochastic performance measures (or criteria). Incorporating such multivariate preference
Noyan, Nilay, Rudolf, Gabor
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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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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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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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四川大学学报. 自然科学版, 2021 This paper studies a version of vehicle routing problem with spatial-temporal correlated stochastic travel times in real road networks. First,a two-stage stochastic optimization model is established for this problem.
ZHANG Dong-Qing +2 more
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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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Journal of Flood Risk Management, 2021 This study presents a novel stochastic simulation–optimization approach for optimum designing of flood control dam through incorporation of various sources of uncertainties. The optimization problem is formulated based on two objective functions, namely,
Ahmad Sharafati +2 more
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An Accelerated Method For Decentralized Distributed Stochastic Optimization Over Time-Varying Graphs [PDF]
, 2022 We consider a distributed stochastic optimization problem that is solved by a decentralized network of agents with only local communication between neighboring agents. The goal of the whole system is to minimize a global objective function given as a sum
Dvurechensky, Pavel +4 more
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Optimization of stochastic systems [PDF]
Proceedings of the 18th conference on Winter simulation - WSC '86, 1986 This paper gives a short survey of Monte Carlo algorithms for stochastic optimization. Both discrete and continuous parameter stochastic optimization are discussed, with emphasis on the analysis of convergence rate. Some future research directions for the area are also indicated.
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