Fetching the paper…
Reading the bibliography…
We extend the classic convergence rate theory for subgradient methods to apply to non-Lipschitz functions.
Minimization Methods for Non-Differentiable Functions
Naun Zuselevich Shor · 1985
Earlier work this paper cites.
Error bounds and convergence analysis of feasible descent methods: a general approach
Zhi-Quan Luo and Paul Tseng · 1993
Earlier work this paper cites.
Introductory Lectures on Convex Optimization: A Basic Course
Yurii Nesterov · 2004
Earlier work this paper cites.
Efficient Online and Batch Learning Using Forward Backward Splitting
John Duchi and Yoram Singer · 2009
Earlier work this paper cites.
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning
Jin Yu, S.V.N. Vishwanathan, Simon Günter, and Nicol N. Schraudolph · 2010
Earlier work this paper cites.
Pegasos: primal estimated sub-gradient solver for svm
Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro, and Andrew Cotter · 2011
Earlier work this paper cites.
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method
Simon Lacoste-Julien, Mark Schmidt, and Francis Bach · 2012
Cited alongside, same era.
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization
Alexander Rakhlin, Ohad Shamir, and Karthik Sridharan · 2012
Cited alongside, same era.
Beyond the Regret Minimization Barrier: Optimal Algorithms for Stochastic Strongly-convex Optimization
Elad Hazan and Satyen Kale · 2014
Cited alongside, same era.
On stochastic subgradient mirror-descent algorithm with weighted averaging
Angelia Nedić and Soomin Lee · 2014
Cited alongside, same era.
Convex Optimization: Algorithms and Complexity
Sébastien Bubeck · 2015
Cited alongside, same era.
Error bounds, quadratic growth, and linear convergence of proximal methods
“Efficient” Subgradient Methods for General Convex Optimization
James Renegar · 2016
Later among the works it cites.
From error bounds to the complexity of first-order descent methods for convex functions
Jérôme Bolte, Trong Phong Nguyen, Juan Peypouquet, and Bruce W. Suter · 2017
Closest in time.
Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems
Damek Davis and Benjamin Grimmer · 2017
Closest in time.
“Relative-Continuity” for Non-Lipschitz Non-Smooth Convex Optimization using Stochastic (or Deterministic) Mirror Descent
Haihao Lu · 2017
Closest in time.
Radial Subgradient Method
Benjamin Grimmer · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dima Drusvyatskiy and Adrian Lewis · 2016
Cited alongside, same era.
Linear convergence of first order methods for non-strongly convex optimization
Ion Necoara, Yurii Nesterov, and Francois Glineur
Cited in the paper.