Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction

In this paper, we propose a stochastic method for solving equality constrained optimization problems that utilizes predictive variance reduction. Specifically, we develop a method based on the sequential quadratic programming paradigm that employs variance reduction in the gradient approximations. U...

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Bibliographic Details
Published in:	Computational optimization and applications Vol. 86; no. 1; pp. 79 - 116
Main Authors:	Berahas, Albert S., Shi, Jiahao, Yi, Zihong, Zhou, Baoyu
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.09.2023 Springer Nature B.V
Subjects:	Algorithms Approximation Convex and Discrete Geometry Machine learning Management Science Mathematics Mathematics and Statistics Methods Neighborhoods Operations Research Operations Research/Decision Theory Optimization Quadratic programming Reduction Statistics Sequential quadratic programming (SQP) Equality constraints Constrained stochastic optimization Variance reduction
ISSN:	0926-6003, 1573-2894
Online Access:	Get full text
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