Hybrid SGD algorithms to solve stochastic composite optimization problems with application in sparse portfolio selection problems

DOI10.1016/j.cam.2023.115425OpenAlexW4382199935MaRDI QIDQ6049347

Zhen-Ping Yang, Yong Zhao

Publication date: 15 September 2023

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2023.115425

zbMATH Keywords

complexityprox-linear algorithmhybrid stochastic estimatornormalized proximal gradient algorithmstochastic nonsmooth composite optimization

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Nonconvex programming, global optimization (90C26) Randomized algorithms (68W20)

Cites Work

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• Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions
• On smoothing, regularization, and averaging in stochastic approximation methods for stochastic variational inequality problems
• Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization
• Stochastic Gauss-Newton algorithm with STORM estimators for nonconvex composite optimization
• A hybrid stochastic optimization framework for composite nonconvex optimization
• Incremental constraint projection methods for variational inequalities
• Variance-Based Extragradient Methods with Line Search for Stochastic Variational Inequalities
• Stochastic Model-Based Minimization of Weakly Convex Functions
• Multilevel Stochastic Gradient Methods for Nested Composition Optimization
• Accelerating Stochastic Composition Optimization
• Stochastic Approximation Approaches to the Stochastic Variational Inequality Problem
• MultiLevel Composite Stochastic Optimization via Nested Variance Reduction
• A Stochastic Subgradient Method for Nonsmooth Nonconvex Multilevel Composition Optimization
• Stochastic Multilevel Composition Optimization Algorithms with Level-Independent Convergence Rates
• The importance of better models in stochastic optimization
• Incremental Constraint Projection Methods for Monotone Stochastic Variational Inequalities
• A Single Timescale Stochastic Approximation Method for Nested Stochastic Optimization
• Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity
• Regularized Iterative Stochastic Approximation Methods for Stochastic Variational Inequality Problems
• Extragradient Method with Variance Reduction for Stochastic Variational Inequalities
• Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization

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