Lock-Free Optimization for Non-Convex Problems

Shen-Yi Zhao; Gong-Duo Zhang; Wu-Jun Li

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arXiv · 1612.03441

Lock-Free Optimization for Non-Convex Problems

Shen-Yi Zhao·Gong-Duo Zhang·Wu-Jun Li

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Abstract

Stochastic gradient descent~(SGD) and its variants have attracted much attention in machine learning due to their efficiency and effectiveness for optimization. To handle large-scale problems, researchers have recently proposed several lock-free strategy based parallel SGD~(LF-PSGD) methods for multi-core systems. However, existing works have only proved the convergence of these LF-PSGD methods for convex problems. To the best of our knowledge, no work has proved the convergence of the LF-PSGD methods for non-convex problems. In this paper, we provide the theoretical proof about the convergence of two representative LF-PSGD methods, Hogwild! and AsySVRG, for non-convex problems. Empirical results also show that both Hogwild! and AsySVRG are convergent on non-convex problems, which successfully verifies our theoretical results.

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BibTeX RIS

Shen-Yi Zhao, Gong-Duo Zhang, Wu-Jun Li. 2016-12-11. Lock-Free Optimization for Non-Convex Problems. https://arxiv.org/abs/1612.03441

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