Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients

1 June 2018

Papers citing "Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients"

11 / 11 papers shown

Title
Second-order Conditional Gradient Sliding Alejandro Carderera Sebastian Pokutta 50 13 0 20 Feb 2020
Generalized Self-Concordant Functions: A Recipe for Newton-Type Methods Tianxiao Sun Quoc Tran-Dinh 54 61 0 14 Mar 2017
CoCoA: A General Framework for Communication-Efficient Distributed Optimization Virginia Smith Simone Forte Chenxin Ma Martin Takáč Michael I. Jordan Martin Jaggi 66 273 0 07 Nov 2016
Optimization Methods for Large-Scale Machine Learning Léon Bottou Frank E. Curtis J. Nocedal 209 3,202 0 15 Jun 2016
Second-Order Stochastic Optimization for Machine Learning in Linear Time Naman Agarwal Brian Bullins Elad Hazan ODL 46 102 0 12 Feb 2016
Practical Inexact Proximal Quasi-Newton Method with Global Complexity Analysis K. Scheinberg Xiaocheng Tang 68 82 0 26 Nov 2013
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) Francis R. Bach Eric Moulines 87 405 0 10 Jun 2013
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances Marco Cuturi OT 178 4,251 0 04 Jun 2013
Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression Francis R. Bach 75 164 0 25 Mar 2013
Self-concordant analysis for logistic regression Francis R. Bach 177 208 0 24 Oct 2009
Robust Regression and Lasso Huan Xu Constantine Caramanis Shie Mannor OOD 86 302 0 11 Nov 2008