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Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization

Xiao Wang (wangxiao***at***ucas.ac.cn)
Shiqian Ma (sqma***at***se.cuhk.edu.hk)
Wei Liu (weiliu***at***us.ibm.com)

Abstract: In this paper we study stochastic quasi-Newton methods for nonconvex stochastic optimization, where we assume that only stochastic information of the gradients of the objective function is available via a stochastic first-order oracle (SFO). Firstly, we propose a general framework of stochastic quasi-Newton methods for solving nonconvex stochastic optimization. The proposed framework extends the classic quasi-Newton methods working in deterministic settings to stochastic settings, and we prove its almost sure convergence to stationary points. Secondly, we propose a general framework for a class of randomized stochastic quasi-Newton methods, in which the number of iterations conducted by the algorithm is a random variable. The worst-case SFO-calls complexities of this class of methods are analyzed. Thirdly, we present two specific methods that fall into this framework, namely stochastic damped-BFGS method and stochastic cyclic Barzilai-Borwein method. Finally, we report numerical results to demonstrate the efficiency of the proposed methods.

Keywords: Stochastic Optimization, Nonconvex Optimization, Stochastic Approximation, Quasi-Newton Method, BFGS Method, Barzilai-Borwein Method, Complexity

Category 1: Nonlinear Optimization

Category 2: Stochastic Programming

Citation:

Download: [PDF]

Entry Submitted: 12/02/2014
Entry Accepted: 12/03/2014
Entry Last Modified: 12/03/2014

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