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In this paper, we propose a stochastic method for solving equalityconstrained optimization problems that utilizes predictive variance reduction.Specifically, we develop a method based on the sequential quadratic programmingparadigm that employs variance reduction in the gradient approximations. Underreasonable assumptions, we prove that a measure of first-order stationarityevaluated at the iterates generated by our proposed algorithm converges to zeroin expectation from arbitrary starting points, for both constant and adaptivestep size strategies. Finally, we demonstrate the practical performance of ourproposed algorithm on constrained binary classification problems that arise inmachine learning.

Accelerating Stochastic Sequential Quadratic Programming for Equality Constrained Optimization using Predictive Variance Reduction