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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.

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This article was published in the following journal.

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

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