A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions

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Michel Benaïm, Josef Hofbauer, and Sylvain Sorin · 2005

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A robust gradient sampling algorithm for nonsmooth, nonconvex optimization

James V Burke, Adrian S Lewis, and Michael L Overton · 2005

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Convergence of the gradient sampling algorithm for nonsmooth nonconvex optimization

Krzysztof C Kiwiel · 2007

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Stochastic first-and zeroth-order methods for nonconvex stochastic programming

Saeed Ghadimi and Guanghui Lan · 2013

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Stochastic variance reduction for nonconvex optimization

Sashank J Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, and Alex Smola · 2016

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How to escape saddle points efficiently

Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M Kakade, and Michael I Jordan · 2017

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How to make the gradients small stochastically: Even faster convex and nonconvex sgd

Zeyuan Allen-Zhu · 2018

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Accelerated methods for nonconvex optimization

Yair Carmon, John C Duchi, Oliver Hinder, and Aaron Sidford · 2018

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Subgradient methods for sharp weakly convex functions

Damek Davis, Dmitriy Drusvyatskiy, Kellie J MacPhee, and Courtney Paquette · 2018

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Spider: Near-optimal non-convex optimization via stochastic path-integrated differential estimator

Cong Fang, Chris Junchi Li, Zhouchen Lin, and Tong Zhang · 2018

A mathematical model for automatic differentiation in machine learning

Original

Jerome Bolte and Edouard Pauwels · 2020

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Gradient sampling methods for nonsmooth optimization

James V Burke, Frank E Curtis, Adrian S Lewis, Michael L Overton, and Lucas EA Simões · 2020

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Pathological subgradient dynamics

Aris Daniilidis and Dmitriy Drusvyatskiy · 2020

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Stochastic subgradient method converges on tame functions

Damek Davis, Dmitriy Drusvyatskiy, Sham Kakade, and Jason D Lee · 2020

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Can we find near-approximately-stationary points of nonsmooth nonconvex functions?

Original

Ohad Shamir · 2020

Later among the works it cites.