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

Albert S. Berahas (), Jiahao Shi (), Zihong Yi () and Baoyu Zhou ()
Additional contact information
Albert S. Berahas: University of Michigan
Jiahao Shi: University of Michigan
Zihong Yi: University of Michigan
Baoyu Zhou: University of Chicago

Computational Optimization and Applications, 2023, vol. 86, issue 1, No 3, 79-116

Abstract: Abstract 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. Under reasonable assumptions, we prove that a measure of first-order stationarity evaluated at the iterates generated by our proposed algorithm converges to zero in expectation from arbitrary starting points, for both constant and adaptive step size strategies. Finally, we demonstrate the practical performance of our proposed algorithm on constrained binary classification problems that arise in machine learning.

Keywords: Constrained stochastic optimization; Equality constraints; Sequential quadratic programming (SQP); Variance reduction (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10589-023-00483-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:coopap:v:86:y:2023:i:1:d:10.1007_s10589-023-00483-2

Ordering information: This journal article can be ordered from
http://www.springer.com/math/journal/10589

DOI: 10.1007/s10589-023-00483-2

Access Statistics for this article

Computational Optimization and Applications is currently edited by William W. Hager

More articles in Computational Optimization and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().