A sequential quadratic programming method with high-probability complexity bounds for nonlinear equality-constrained stochastic optimization
DOI10.1137/23m1549006MaRDI QIDQ6663117
Baoyu Zhou, Miaolan Xie, Albert S. Berahas
Publication date: 14 January 2025
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
nonlinear optimizationconstrained stochastic optimizationsequential quadratic optimizationprobabilistic oraclesstep search
Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Numerical optimization and variational techniques (65K10) Quadratic programming (90C20) Stochastic programming (90C15) Methods of successive quadratic programming type (90C55) Numerical methods in optimal control (49Mxx)
Cites Work
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- Global convergence rate analysis of unconstrained optimization methods based on probabilistic models
- Stochastic optimization using a trust-region method and random models
- Generalized stochastic Frank-Wolfe algorithm with stochastic ``substitute gradient for structured convex optimization
- Linesearch Newton-CG methods for convex optimization with noise
- First-order and stochastic optimization methods for machine learning
- CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization
- Convergence of Trust-Region Methods Based on Probabilistic Models
- Optimal Solvers for PDE-Constrained Optimization
- An Inexact SQP Method for Equality Constrained Optimization
- Complexity and global rates of trust-region methods based on probabilistic models
- Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods
- Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization
- Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise
- Lectures on Stochastic Programming: Modeling and Theory, Third Edition
- Benchmarking Derivative-Free Optimization Algorithms
- A Stochastic Line Search Method with Expected Complexity Analysis
- Line Search Filter Methods for Nonlinear Programming: Motivation and Global Convergence
- Direct Search Based on Probabilistic Descent
- An adaptive stochastic sequential quadratic programming with differentiable exact augmented Lagrangians
- Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming
- A trust region method for noisy unconstrained optimization
- Direct Search Based on Probabilistic Descent in Reduced Spaces
- Worst-case complexity of an SQP method for nonlinear equality constrained stochastic optimization
- Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction
- Constrained Optimization in the Presence of Noise
- High probability complexity bounds for adaptive step search based on stochastic oracles
- First- and second-order high probability complexity bounds for trust-region methods with noisy oracles
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