Web: http://arxiv.org/abs/2104.14840

Jan. 14, 2022, 2:11 a.m. | Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang

cs.LG updates on arXiv.org arxiv.org

In this paper, we consider the widely used but not fully understood
stochastic estimator based on moving average (SEMA), which only requires {\bf a
general unbiased stochastic oracle}. We demonstrate the power of SEMA on a
range of stochastic non-convex optimization problems. In particular, we analyze
various stochastic methods (existing or newly proposed) based on the {\bf
variance recursion property} of SEMA for three families of non-convex
optimization, namely standard stochastic non-convex minimization, stochastic
non-convex strongly-concave min-max optimization, and stochastic bilevel
optimization. Our contributions include: (i) for standard stochastic non-convex …

arxiv for math on optimization stochastic

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