Convergence of a stochastic subgradient method with averaging for nonsmooth nonconvex constrained optimization

DOI10.1007/s11590-020-01537-8zbMath1459.90168arXiv1912.07580OpenAlexW3002206944WikidataQ126300885 ScholiaQ126300885MaRDI QIDQ2228354

Ruszczyński, Andrzej

Publication date: 17 February 2021

Published in: Optimization Letters (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1912.07580

zbMATH Keywords

nonsmooth optimizationchain rulestochastic subgradient methodgeneralized differentiable functions

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26)

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A Bundle Trust Region Algorithm for Minimizing Locally Lipschitz Functions ⋮ Convergence of the proximal bundle algorithm for nonsmooth nonconvex optimization problems ⋮ Convergence of constant step stochastic gradient descent for non-smooth non-convex functions ⋮ A stochastic subgradient method for distributionally robust non-convex and non-smooth learning ⋮ Stochastic Multilevel Composition Optimization Algorithms with Level-Independent Convergence Rates ⋮ Conservative and semismooth derivatives are equivalent for semialgebraic maps ⋮ Subgradient Sampling for Nonsmooth Nonconvex Minimization ⋮ Mini-Batch Risk Forms ⋮ A Decomposition Algorithm for Two-Stage Stochastic Programs with Nonconvex Recourse Functions ⋮ A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning ⋮ A Stochastic Subgradient Method for Nonsmooth Nonconvex Multilevel Composition Optimization

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• Stochastic Methods for Composite and Weakly Convex Optimization Problems
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• Stochastic Approximations and Differential Inclusions

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