Results 1 to 10 of about 2,320,712 (255)
Randomized Smoothing for Stochastic Optimization [PDF]
SIAM Journal on Optimization, 2012 39 pages, 3 ...
Martin J Wainwright, Peter Bartlett
exaly +5 more sources
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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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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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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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
doaj +1 more source
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
doaj +1 more source
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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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
doaj +1 more source
A New Stochastic Process of Prestack Inversion for Rock Property Estimation
Applied Sciences, 2022 In order to enrich the current prestack stochastic inversion theory, we propose a prestack stochastic inversion method based on adaptive particle swarm optimization combined with Markov chain Monte Carlo (MCMC).
Long Yin +6 more
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Effects of Probability Function on the Performance of Stochastic Programming [PDF]
Journal of Optimization in Industrial Engineering, 2018 Stochastic programming is a valuable optimization tool where used when some or all of the design parameters of an optimization problem are defined by stochastic variables rather than by deterministic quantities.
Mohammad Ebrahim Karbaschi +1 more
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