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arXiv · 1910.07939

A Stochastic Variance Reduced Nesterov's Accelerated Quasi-Newton Method

Sota Yasuda·Shahrzad Mahboubi·S. Indrapriyadarsini·Hiroshi Ninomiya·Hideki Asai
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Abstract

Recently algorithms incorporating second order curvature information have become popular in training neural networks. The Nesterov's Accelerated Quasi-Newton (NAQ) method has shown to effectively accelerate the BFGS quasi-Newton method by incorporating the momentum term and Nesterov's accelerated gradient vector. A stochastic version of NAQ method was proposed for training of large-scale problems. However, this method incurs high stochastic variance noise. This paper proposes a stochastic variance reduced Nesterov's Accelerated Quasi-Newton method in full (SVR-NAQ) and limited (SVRLNAQ) memory forms. The performance of the proposed method is evaluated in Tensorflow on four benchmark problems - two regression and two classification problems respectively. The results show improved performance compared to conventional methods.

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Sota Yasuda, Shahrzad Mahboubi, S. Indrapriyadarsini, Hiroshi Ninomiya, Hideki Asai. 2019-10-17. A Stochastic Variance Reduced Nesterov's Accelerated Quasi-Newton Method. https://doi.org/10.1109/icmla.2019.00301

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