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• W3105816804 abstract "We study decentralized non-convex finite-sum minimization problems described over a network of nodes, where each node possesses a local batch of data samples. In this context, we analyze a single-timescale randomized incremental gradient method, called GT-SAGA. GT-SAGA is computationally efficient as it evaluates one component gradient per node per iteration and achieves provably fast and robust performance by leveraging node-level variance reduction and network-level gradient tracking. For general smooth non-convex problems, we show the almost sure and mean-squared convergence of GT-SAGA to a first-order stationary point and further describe regimes of practical significance where it outperforms the existing approaches and achieves a network topology-independent iteration complexity respectively. When the global function satisfies the Polyak-Lojaciewisz condition, we show that GT-SAGA exhibits linear convergence to an optimal solution in expectation and describe regimes of practical interest where the performance is network topology-independent and improves upon the existing methods. Numerical experiments are included to highlight the main convergence aspects of GT-SAGA in non-convex settings." @default.
• W3105816804 created "2020-11-23" @default.
• W3105816804 creator A5025641667 @default.
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• W3105816804 date "2020-11-07" @default.
• W3105816804 modified "2023-10-18" @default.
• W3105816804 title "A fast randomized incremental gradient method for decentralized non-convex optimization" @default.
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• W3105816804 doi "https://doi.org/10.48550/arxiv.2011.03853" @default.
• W3105816804 hasPublicationYear "2020" @default.
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