Results 1 to 10 of about 67,878 (263)
Randomized Smoothing for Stochastic Optimization [PDF]
SIAM Journal on Optimization, 2012 39 pages, 3 ...
John C Duchi +2 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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The importance of better models in stochastic optimization [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2019 Hilal Asi, John C Duchi
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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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K-adaptability in stochastic optimization [PDF]
Mathematical Programming, 2022 AbstractWe consider stochastic problems in which both the objective function and the feasible set are affected by uncertainty. We address these problems using a K-adaptability approach, in which K solutions for a given problem are computed before the uncertainty dissolves and afterwards the best of them can be chosen for the realized scenario.
Malaguti E., Monaci M., Pruente J.
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Optimal Stochastic Planarization [PDF]
2010 IEEE 51st Annual Symposium on Foundations of Computer Science, 2010 It has been shown by Indyk and Sidiropoulos [IS07] that any graph of genus g>0 can be stochastically embedded into a distribution over planar graphs with distortion 2^O(g). This bound was later improved to O(g^2) by Borradaile, Lee and Sidiropoulos [BLS09]. We give an embedding with distortion O(log g), which is asymptotically optimal.
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An Isometric Stochastic Optimizer
CoRR, 2023 The Adam optimizer is the standard choice in deep learning applications. I propose a simple explanation of Adam's success: it makes each parameter's step size independent of the norms of the other parameters. Based on this principle I derive Iso, a new optimizer which makes the norm of a parameter's update invariant to the application of any linear ...
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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. We propose a measure, termed variation budget, that controls the extent of said change, and study how restrictions on this budget impact achievable performance.
Omar Besbes, Yonatan Gur, Assaf Zeevi
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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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