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• W3177007066 abstract "In this letter, we propose Push-SAGA, a decentralized stochastic first-order method for finite-sum minimization over a directed network of nodes. Push-SAGA combines node-level variance reduction to remove the uncertainty caused by stochastic gradients, network-level gradient tracking to address the distributed nature of the data, and push-sum consensus to tackle directed information exchange. We show that Push-SAGA achieves linear convergence to the exact solution for smooth and strongly convex problems and is thus the first linearly-convergent stochastic algorithm over arbitrary strongly connected directed graphs. We also characterize the regime in which Push-SAGA achieves a linear speed-up compared to its centralized counterpart and achieves a network-independent convergence rate. We illustrate the behavior and convergence properties of Push-SAGA with the help of numerical experiments on strongly convex and non-convex problems." @default.
• W3177007066 created "2021-07-05" @default.
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• W3177007066 date "2022-01-01" @default.
• W3177007066 modified "2023-10-18" @default.
• W3177007066 title "Push-SAGA: A Decentralized Stochastic Algorithm With Variance Reduction Over Directed Graphs" @default.
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• W3177007066 doi "https://doi.org/10.1109/lcsys.2021.3090652" @default.
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