Algorithms for stochastic optimization with function or expectation constraints
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Publication:2181600
DOI10.1007/s10589-020-00179-xzbMath1443.90263arXiv1604.03887OpenAlexW3007099432MaRDI QIDQ2181600
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
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
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