An Accelerated Linearly Convergent Stochastic L-BFGS Algorithm
Daqing Chang, Shiliang Sun, Changshui Zhang · IEEE Transactions on Neural Networks and Learning Systems · 2019
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.