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A Simple Linear Convergence Analysis of the Point-SAGA Algorithm
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Point-SAGA is a randomized algorithm for minimizing a sum of convex functions using their proximity operators (proxs), proposed by Defazio (2016). At every iteration, the prox of only one randomly chosen function is called. We generalize the algorithm to any number of prox calls per iteration, not only one, and propose a simple proof of linear convergence when the functions are smooth and strongly convex.
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The Stochastic Multi-Proximal Method for Nonsmooth Optimization
SMPM is a stochastic multi-proximal method that recovers several existing algorithms as special cases and provides new linear and accelerated sublinear convergence guarantees for nonsmooth convex problems.
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