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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.
Besbes, O., Gur, Y., Zeevi, A.
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FROST—Fast row-stochastic optimization with uncoordinated step-sizes
EURASIP Journal on Advances in Signal Processing, 2019 In this paper, we discuss distributed optimization over directed graphs, where doubly stochastic weights cannot be constructed. Most of the existing algorithms overcome this issue by applying push-sum consensus, which utilizes column-stochastic weights ...
Ran Xin, Chenguang Xi, Usman A. Khan
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Distributed Delayed Stochastic Optimization [PDF]
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2011 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
Agarwal, Alekh, Duchi, John C.
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An intelligent stochastic optimization approach for air cargo order allocation under carbon emission constraints. [PDF]
PLoS ONEIn air cargo transportation, effective order allocation is crucial for improving the efficiency of business operations and reducing environmental impact.
Zhenzhong Zhang +3 more
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Stochastic Optimization for an Analytical Model of Saltwater Intrusion in Coastal Aquifers. [PDF]
PLoS ONE, 2016 The present study implements a stochastic optimization technique to optimally manage freshwater pumping from coastal aquifers. Our simulations utilize the well-known sharp interface model for saltwater intrusion in coastal aquifers together with its ...
Paris N Stratis +4 more
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Asymptotic optimality in stochastic optimization [PDF]
The Annals of Statistics, 2021 We study local complexity measures for stochastic convex optimization problems, providing a local minimax theory analogous to that of Hájek and Le Cam for classical statistical problems. We give complementary optimality results, developing fully online methods that adaptively achieve optimal convergence guarantees. Our results provide function-specific
Duchi, John C., Ruan, Feng
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Transactions of the Institute of Systems, Control and Information Engineers, 2023 to appear in the Transactions of the Institute of Systems, Control and Information Engineers 36 (2023)
Fukushima-Kimura, Bruno Hideki +3 more
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Stochastic polynomial optimization [PDF]
Optimization Methods and Software, 2019 This paper studies stochastic optimization problems with polynomials. We propose an optimization model with sample averages and perturbations. The Lasserre type Moment-SOS relaxations are used to solve the sample average optimization. Properties of the optimization and its relaxations are studied. Numerical experiments are presented.
Nie, Jiawang, Yang, Liu, Zhong, Suhan
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Information Loss Due to the Data Reduction of Sample Data from Discrete Distributions
Data, 2020 In this paper, we study the information lost when a real-valued statistic is used to reduce or summarize sample data from a discrete random variable with a one-dimensional parameter. We compare the probability that a random sample gives a particular data
Maryam Moghimi, Herbert W. Corley
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Probabilistic Optimization Techniques in Smart Power System
Energies, 2022 Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy.
Muhammad Riaz +4 more
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