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We analyze stochastic gradient algorithms for optimizing nonconvex, nonsmooth finite-sum problems.
Gradient methods for minimizing functionals
Boris Teodorovich Polyak · 1963
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Error bounds and convergence analysis of feasible descent methods: a general approach
Zhi-Quan Luo and Paul Tseng · 1993
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Degenerate nonlinear programming with a quadratic growth condition
Mihai Anitescu · 2000
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Introductory Lectures on Convex Optimization: A Basic Course
Yurii Nesterov · 2004
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Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Saeed Ghadimi and Guanghui Lan · 2013
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Saga: A fast incremental gradient method with support for non-strongly convex composite objectives
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Linear convergence of first order methods for non-strongly convex optimization
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Non-convex finite-sum optimization via scsg methods
Lihua Lei, Cheng Ju, Jianbo Chen, and Michael I Jordan · 2017
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Recovery guarantees for one-hidden-layer neural networks
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Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization
Sashank J Reddi, Suvrit Sra, Barnabás Póczos, and Alexander J Smola
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