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Gradient Descent and the Power Method: Exploiting their connection to
  find the leftmost eigen-pair and escape saddle points

Gradient Descent and the Power Method: Exploiting their connection to find the leftmost eigen-pair and escape saddle points

2 November 2022
R. Tappenden
Martin Takáč
ArXiv (abs)PDFHTML

Papers citing "Gradient Descent and the Power Method: Exploiting their connection to find the leftmost eigen-pair and escape saddle points"

5 / 5 papers shown
Title
Trust-Region Algorithms for Training Responses: Machine Learning Methods
  Using Indefinite Hessian Approximations
Trust-Region Algorithms for Training Responses: Machine Learning Methods Using Indefinite Hessian Approximations
Jennifer B. Erway
J. Griffin
Roummel F. Marcia
Riadh Omheni
49
24
0
01 Jul 2018
Accelerated Stochastic Power Iteration
Accelerated Stochastic Power Iteration
Christopher De Sa
Bryan D. He
Ioannis Mitliagkas
Christopher Ré
Peng Xu
88
91
0
10 Jul 2017
Gradient Descent Can Take Exponential Time to Escape Saddle Points
Gradient Descent Can Take Exponential Time to Escape Saddle Points
S. Du
Chi Jin
Jason D. Lee
Michael I. Jordan
Barnabás Póczós
Aarti Singh
75
244
0
29 May 2017
How to Escape Saddle Points Efficiently
How to Escape Saddle Points Efficiently
Chi Jin
Rong Ge
Praneeth Netrapalli
Sham Kakade
Michael I. Jordan
ODL
237
838
0
02 Mar 2017
Geometric descent method for convex composite minimization
Geometric descent method for convex composite minimization
Shixiang Chen
Shiqian Ma
Wei Liu
63
10
0
29 Dec 2016
1