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arXiv · 1712.00984

Inertial Proximal Incremental Aggregated Gradient Method

Xiaoya Zhang·Wei Peng·Hui Zhang·Wei Zhu
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Abstract

In this paper, we introduce an inertial version of the Proximal Incremental Aggregated Gradient method (PIAG) for minimizing the sum of smooth convex component functions and a possibly nonsmooth convex regularization function. Theoretically, we show that the inertial Proximal Incremental Aggregated Gradiend (iPIAG) method enjoys a global linear convergence under a quadratic growth condition, which is strictly weaker than strong convexity, provided that the stepsize is not larger than a constant. Moreover, we present two numerical expreiments which demonstrate that iPIAG outperforms the original PIAG.

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Xiaoya Zhang, Wei Peng, Hui Zhang, Wei Zhu. 2017-12-04. Inertial Proximal Incremental Aggregated Gradient Method. https://arxiv.org/abs/1712.00984

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