A simple proximal stochastic gradient method for nonsmooth nonconvex optimization

We analyze stochastic gradient algorithms for optimizing nonconvex, nonsmooth finite-sum problems. In particular, the objective function is given by the summation of a differentiable (possibly nonconvex) component, together with a possibly non-differentiable but convex component. We propose a proxim...

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Bibliographic Details
Main Authors: LI, Zhize, LI, Jian
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/8674
https://ink.library.smu.edu.sg/context/sis_research/article/9677/viewcontent/NeurIPS_2018_a_simple_proximal_stochastic_gradient_method_for_nonsmooth_nonconvex_optimization_Paper.pdf
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Institution: Singapore Management University
Language: English