Results 31 to 40 of about 68,699 (263)
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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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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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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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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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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Chebyshev wavelet-based method for solving various stochastic optimal control problems and its application in finance [PDF]
Iranian Journal of Numerical Analysis and OptimizationIn this paper, a computational method based on parameterizing state and control variables is presented for solving Stochastic Optimal Control (SOC) problems.
M. Yarahmadi, S. Yaghobipour
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Stochastic Variational Optimization
CoRR, 2018 Variational Optimization forms a differentiable upper bound on an objective. We show that approaches such as Natural Evolution Strategies and Gaussian Perturbation, are special cases of Variational Optimization in which the expectations are approximated by Gaussian sampling.
Thomas Bird, Julius Kunze, David Barber
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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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Optimal Stochastic Enhancement of Photoionization [PDF]
Physical Review Letters, 2007 The effect of noise on the nonlinear photoionization of an atom due to a femtosecond pulse is investigated in the framework of the stochastic Schrödinger equation. A modest amount of white noise results in an enhancement of the net ionization yield by several orders of magnitude, giving rise to a form of quantum stochastic resonance.
Singh, K., Rost, J.
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The optimality of (stochastic) veto delegation
Games and Economic Behavior, 2023 We analyze the optimal delegation problem between a principal and an agent, assuming that the latter has state-independent preferences. We demonstrate that if the principal is more risk-averse than the agent toward non-status quo options, an optimal mechanism is a {\em veto mechanism}. In a veto mechanism, the principal uses veto (i.e., maintaining the
Xiaoxiao Hu, Haoran Lei
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