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Author

Ze-Hui Zhou

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Preprint Oct 2026

On the convergence of doubly stochastic block gradient descent method for linear inverse problems

Stochastic gradient descent (SGD) and randomized block coordinate descent (RBCD) belong to two fundamentally different classes of stochastic iterative regularization methods, and are promising and popular for solving large-scale inverse problems, due to their excellent scalability with respect to data size and signal s...

Yu-Xin Xia, Ze-Hui Zhou · 0 citations
Preprint Sep 2026

Early stopping of stochastic variance reduced gradient for linear inverse problems by the discrepancy principle

Stochastic variance reduced gradient (SVRG) is a variant of stochastic gradient descent and is a promising iterative method for solving large-scale inverse problems. Nevertheless, the development of theoretically grounded a posteriori stopping rules for SVRG remains an open challenge. In this work, we provide a converg...

Bang-Ti Jin, Ze-Hui Zhou · 0 citations

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