Algorithms for stochastic optimization with function or expectation constraints

DOI10.1007/s10589-020-00179-xzbMath1443.90263arXiv1604.03887OpenAlexW3007099432MaRDI QIDQ2181600

Guanghui Lan, Zhiqiang Zhou

Publication date: 19 May 2020

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1604.03887

zbMATH Keywords

complexitystochastic optimizationsubgradient methodconvex programming

Mathematics Subject Classification ID

Semidefinite programming (90C22) Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Stochastic programming (90C15) Numerical methods based on nonlinear programming (49M37)

Related Items (14)

Complexity of an inexact proximal-point penalty method for constrained smooth non-convex optimization ⋮ The CoMirror algorithm with random constraint sampling for convex semi-infinite programming ⋮ Adaptive primal-dual stochastic gradient method for expectation-constrained convex stochastic programs ⋮ Solving Stochastic Optimization with Expectation Constraints Efficiently by a Stochastic Augmented Lagrangian-Type Algorithm ⋮ A stochastic primal-dual method for a class of nonconvex constrained optimization ⋮ Primal-dual mirror descent method for constraint stochastic optimization problems ⋮ An adaptive sampling augmented Lagrangian method for stochastic optimization with deterministic constraints ⋮ A relaxation-based probabilistic approach for PDE-constrained optimization under uncertainty with pointwise state constraints ⋮ Stochastic inexact augmented Lagrangian method for nonconvex expectation constrained optimization ⋮ Primal-Dual Stochastic Gradient Method for Convex Programs with Many Functional Constraints ⋮ Recent theoretical advances in decentralized distributed convex optimization ⋮ On a multistage discrete stochastic optimization problem with stochastic constraints and nested sampling ⋮ An asymptotically optimal strategy for constrained multi-armed bandit problems ⋮ On Modification of an Adaptive Stochastic Mirror Descent Algorithm for Convex Optimization Problems with Functional Constraints

Uses Software

• Pegasos

Cites Work

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• Accelerated gradient methods for nonconvex nonlinear and stochastic programming
• An optimal method for stochastic composite optimization
• Erratum to: ``Minimizing finite sums with the stochastic average gradient
• The CoMirror algorithm for solving nonsmooth constrained convex problems
• Primal-dual first-order methods with \({\mathcal {O}(1/\varepsilon)}\) iteration-complexity for cone programming
• Pegasos: primal estimated sub-gradient solver for SVM
• Validation analysis of mirror descent stochastic approximation method
• Sample average approximation of expected value constrained stochastic programs
• Introductory lectures on convex optimization. A basic course.
• The Sample Average Approximation Method for Stochastic Discrete Optimization
• On the Solution of Stochastic Optimization and Variational Problems in Imperfect Information Regimes
• Randomized Smoothing for Stochastic Optimization
• Robust Stochastic Approximation Approach to Stochastic Programming
• stochastic quasigradient methods and their application to system optimization^†
• A method of aggregate stochastic subgradients with on-line stepsize rules for convex stochastic programming problems
• Acceleration of Stochastic Approximation by Averaging
• Convergence of Proximal-Like Algorithms
• Bregman Monotone Optimization Algorithms
• Monte Carlo sampling approach to stochastic programming
• Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization I: A Generic Algorithmic Framework
• On Stochastic Subgradient Mirror-Descent Algorithm with Weighted Averaging
• Optimal Primal-Dual Methods for a Class of Saddle Point Problems
• Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization, II: Shrinking Procedures and Optimal Algorithms
• Convex Approximations of Chance Constrained Programs
• Interior Gradient and Proximal Methods for Convex and Conic Optimization
• Robust Portfolio Selection Problems
• A Stochastic Approximation Method

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