Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models

TL;DR

This paper introduces a trust-region sequential quadratic programming method for stochastic optimization with deterministic constraints, achieving convergence to stationary points using random models and eigen steps, with proven guarantees and superior performance.

Contribution

It develops a novel trust-region SQP algorithm utilizing random models and eigen steps for stochastic constrained optimization, with convergence guarantees and improved empirical results.

Findings

Proven global convergence to first- and second-order stationary points.

Demonstrated superior performance over existing stochastic methods.

Validated effectiveness on various benchmark problems.

Abstract

In this work, we consider solving optimization problems with a stochastic objective and deterministic equality constraints. We propose a Trust-Region Sequential Quadratic Programming method to find both first- and second-order stationary points. Our method utilizes a random model to represent the objective function, which is constructed from stochastic observations of the objective and is designed to satisfy proper adaptive accuracy conditions with a high but fixed probability. To converge to first-order stationary points, our method computes a gradient step in each iteration defined by minimizing a quadratic approximation of the objective subject to a (relaxed) linear approximation of the problem constraints and a trust-region constraint. To converge to second-order stationary points, our method additionally computes an eigen step to explore the negative curvature of the reduced Hessian matrix, as well as a second-order correction step to address the potential Maratos effect, which arises due to the nonlinearity of the problem constraints. Such an effect may impede the method from moving away from saddle points. Both gradient and eigen step computations leverage a novel parameter-free decomposition of the step and the trust-region radius, accounting for the proportions among the feasibility residual, optimality residual, and negative curvature. We establish global almost sure first- and second-order convergence guarantees for our method, and present computational results on CUTEst problems, regression problems, and saddle-point problems to demonstrate its superiority over existing line-search-based stochastic methods.