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Nov, 2019
随机ADMM实现的非凸随机嵌套优化
Nonconvex Stochastic Nested Optimization via Stochastic ADMM
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Zhongruo Wang
TL;DR
本研究考虑了随机嵌套组合优化问题,方法使用随机ADMM算法,当期望罚函数子梯度的模小于某一epsilon时,其在线情况下的总样本复杂度为O(epsilon^-3),有限求和情况下为O((2N1+N2) + (2N1+N2)^1/2*epsilon^-2),算法实现相对于现有的基于proximal算法的版本更为通用。
Abstract
We consider the stochastic
nested composition optimization
problem where the objective is a composition of two
expected-value functions
. We proposed the
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