An Accelerated Linearly Convergent Stochastic L-BFGS Algorithm.

07:00 EST 25th January 2019 | BioPortfolio

Summary of "An Accelerated Linearly Convergent Stochastic L-BFGS Algorithm."

The limited memory version of the Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm is the most popular quasi-Newton algorithm in machine learning and optimization. Recently, it was shown that the stochastic L-BFGS (sL-BFGS) algorithm with the variance-reduced stochastic gradient converges linearly. In this paper, we propose a new sL-BFGS algorithm by importing a proper momentum. We prove an accelerated linear convergence rate under mild conditions. The experimental results on different data sets also verify this acceleration advantage.


Journal Details

This article was published in the following journal.

Name: IEEE transactions on neural networks and learning systems
ISSN: 2162-2388


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