A Linearly-Convergent Stochastic L-BFGS Algorithm

Philipp Moritz; Robert Nishihara; Michael I. Jordan

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arXiv · 1508.02087

A Linearly-Convergent Stochastic L-BFGS Algorithm

Philipp Moritz·Robert Nishihara·Michael I. Jordan

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Abstract

We propose a new stochastic L-BFGS algorithm and prove a linear convergence rate for strongly convex and smooth functions. Our algorithm draws heavily from a recent stochastic variant of L-BFGS proposed in Byrd et al. (2014) as well as a recent approach to variance reduction for stochastic gradient descent from Johnson and Zhang (2013). We demonstrate experimentally that our algorithm performs well on large-scale convex and non-convex optimization problems, exhibiting linear convergence and rapidly solving the optimization problems to high levels of precision. Furthermore, we show that our algorithm performs well for a wide-range of step sizes, often differing by several orders of magnitude.

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BibTeX RIS

Philipp Moritz, Robert Nishihara, Michael I. Jordan. 2015-08-09. A Linearly-Convergent Stochastic L-BFGS Algorithm. https://arxiv.org/abs/1508.02087

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