Stochastic nested primal-dual method for nonconvex constrained composition optimization

Publication date: 22 October 2024

Published in: Mathematics of Computation (Search for Journal in Brave)

composition stationarity feasibility augmented Lagrangian function iteration complexity nonconvex constraints complementary slackness sample complexity

Mathematics Subject Classification ID

Nonlinear programming (90C30) Numerical optimization and variational techniques (65K10) Optimality conditions and duality in mathematical programming (90C46)

Cites Work

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Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions
Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization
Complexity of an inexact proximal-point penalty method for constrained smooth non-convex optimization
A stochastic primal-dual method for a class of nonconvex constrained optimization
Algorithms for stochastic optimization with function or expectation constraints
The multiplier method of Hestenes and Powell applied to convex programming
Stochastic first-order methods for convex and nonconvex functional constrained optimization
An augmented Lagrangian trust region method for equality constrained optimization
Penalty methods with stochastic approximation for stochastic nonlinear programming
An augmented Lagrangian affine scaling method for nonlinear programming
Multilevel Stochastic Gradient Methods for Nested Composition Optimization
Accelerating Stochastic Composition Optimization
Orthogonal Nonnegative Matrix Factorization by Sparsity and Nuclear Norm Optimization
MultiLevel Composite Stochastic Optimization via Nested Variance Reduction
Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization
A Stochastic Subgradient Method for Nonsmooth Nonconvex Multilevel Composition Optimization
Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning
Stochastic Multilevel Composition Optimization Algorithms with Level-Independent Convergence Rates
Primal-Dual Stochastic Gradient Method for Convex Programs with Many Functional Constraints
Orthogonal Nonnegative Tucker Decomposition
A Single Timescale Stochastic Approximation Method for Nested Stochastic Optimization
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization
Probability maximization via Minkowski functionals: convex representations and tractable resolution
Worst-case complexity of an SQP method for nonlinear equality constrained stochastic optimization

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