Results 1 to 10 of about 68,699 (263)
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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Randomized Smoothing for Stochastic Optimization [PDF]
SIAM Journal on Optimization, 2012 39 pages, 3 ...
Martin J Wainwright, Peter Bartlett
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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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The importance of better models in stochastic optimization [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2019 John Duchi, Hilal Asi
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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.
Jiawang Nie, Liu Yang, Suhan Zhong
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Stochastic Optimization Forests
Management Science, 2023 We study contextual stochastic optimization problems, where we leverage rich auxiliary observations (e.g., product characteristics) to improve decision making with uncertain variables (e.g., demand). We show how to train forest decision policies for this problem by growing trees that choose splits to directly optimize the downstream decision quality ...
Nathan Kallus, Xiaojie Mao
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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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Numerical simulations for stochastic meme epidemic model
Advances in Difference Equations, 2020 The primary purpose of this study is to perform the comparison of deterministic and stochastic modeling. The effect of threshold number is also observed in this model.
Ali Raza +3 more
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Advances in Difference Equations, 2020 The current effort is devoted to investigating and exploring the stochastic nonlinear mathematical pandemic model to describe the dynamics of the novel coronavirus.
Wasfi Shatanawi +5 more
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