Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization

5 Jul 2016Xiao WangShiqian MaDonald GoldfarbWei Liu

In this paper we study stochastic quasi-Newton methods for nonconvex stochastic optimization, where we assume that noisy information about the gradients of the objective function is available via a stochastic first-order oracle (SFO). We propose a general framework for such methods, for which we prove almost sure convergence to stationary points and analyze its worst-case iteration complexity... (read more)

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