Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

نویسندگان

  • Lam M. Nguyen
  • Jie Liu
  • Katya Scheinberg
  • Martin Takác
چکیده

In this paper, we study and analyze the mini-batch version of StochAstic Recursive grAdient algoritHm (SARAH), a method employing the stochastic recursive gradient, for solving empirical loss minimization for the case of nonconvex losses. We provide a sublinear convergence rate (to stationary points) for general nonconvex functions and a linear convergence rate for gradient dominated functions, both of which have some advantages compared to other modern stochastic gradient algorithms for nonconvex losses.

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عنوان ژورنال:
  • CoRR

دوره abs/1705.07261  شماره 

صفحات  -

تاریخ انتشار 2017