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  • Title: An Accelerated Linearly Convergent Stochastic L-BFGS Algorithm.
    Author: Chang D, Sun S, Zhang C.
    Journal: IEEE Trans Neural Netw Learn Syst; 2019 Nov; 30(11):3338-3346. PubMed ID: 30703047.
    Abstract:
    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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