Convergence Rates for Deterministic and Stochastic Subgradient Methods
Without Lipschitz Continuity

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Convergence Rates for Deterministic and Stochastic Subgradient Methods Without Lipschitz Continuity

12 December 2017

Benjamin Grimmer

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Papers citing "Convergence Rates for Deterministic and Stochastic Subgradient Methods Without Lipschitz Continuity"

6 / 6 papers shown

Title
Understanding Gradient Orthogonalization for Deep Learning via Non-Euclidean Trust-Region Optimization Dmitry Kovalev 110 4 0 16 Mar 2025
Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization Benjamin Grimmer Danlin Li 82 6 0 31 Dec 2024
Directional Smoothness and Gradient Methods: Convergence and Adaptivity Aaron Mishkin Ahmed Khaled Yuanhao Wang Aaron Defazio Robert Mansel Gower 92 9 0 06 Mar 2024
Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems Damek Davis Benjamin Grimmer 53 113 0 12 Jul 2017
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method Simon Lacoste-Julien Mark Schmidt Francis R. Bach 185 260 0 10 Dec 2012
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization Alexander Rakhlin Ohad Shamir Karthik Sridharan 169 768 0 26 Sep 2011