For linear least squares, SNGD and SPRING are proved equivalent to accelerated regularized Kaczmarz methods, yielding the first fast rates and first SPRING guarantee; the general quadratic analysis holds under strong idealized assumptions.
Stochastic (approximate) proximal point methods: Convergence, optimality, and adaptivity
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A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms
For linear least squares, SNGD and SPRING are proved equivalent to accelerated regularized Kaczmarz methods, yielding the first fast rates and first SPRING guarantee; the general quadratic analysis holds under strong idealized assumptions.