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math.OC2023
A new non-convex framework to improve asymptotical knowledge on generic stochastic gradient descent
Jean-Baptiste Fest, Audrey Repetti, Emilie Chouzenoux
Stochastic gradient optimization methods are broadly used to minimize non-convex smooth objective functions, for instance when training deep neural networks. However, theoretical g…
math.OC2023★ 3 cited
A stochastic use of the Kurdyka-Lojasiewicz property: Investigation of optimization algorithms behaviours in a non-convex differentiable framework
Jean-Baptiste Fest, Audrey Repetti, Emilie Chouzenoux
Stochastic differentiable approximation schemes are widely used for solving high dimensional problems. Most of existing methods satisfy some desirable properties, including conditi…