The importance of better models in stochastic optimization

DOI10.1073/PNAS.1908018116zbMath1431.90098arXiv1903.08619OpenAlexW2982573148WikidataQ91010866 ScholiaQ91010866MaRDI QIDQ5218482

Hilal Asi, John C. Duchi

Publication date: 4 March 2020

Published in: Proceedings of the National Academy of Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1903.08619

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Stochastic programming (90C15)

Related Items (6)

Global convergence of model function based Bregman proximal minimization algorithms ⋮ Hybrid SGD algorithms to solve stochastic composite optimization problems with application in sparse portfolio selection problems ⋮ A dual-based stochastic inexact algorithm for a class of stochastic nonsmooth convex composite problems ⋮ Unnamed Item ⋮ A semismooth Newton stochastic proximal point algorithm with variance reduction ⋮ Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization

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