Inertial Proximal Incremental Aggregated Gradient Method

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.

arXiv comment17 pages, 3 figures

References in corpus (5)

• Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server
• New Analysis of Linear Convergence of Gradient-type Methods via Unifying Error Bound Conditions
• A Stronger Convergence Result on the Proximal Incremental Aggregated Gradient Method
• Linear Convergence of the Proximal Incremental Aggregated Gradient Method under Quadratic Growth Condition
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Cited by in corpus (1)

• Proximal-Like Incremental Aggregated Gradient Method with Linear Convergence under Bregman Distance Growth Conditions