Science-advisor
REGISTER info/FAQ
Login
username
password
     
forgot password?
register here
 
Research articles
  search articles
  reviews guidelines
  reviews
  articles index
My Pages
my alerts
  my messages
  my reviews
  my favorites
 
 
Stat
Members: 3667
Articles: 2'599'751
Articles rated: 2609

07 February 2025
 
  » arxiv » 1605.0320

 Article overview



A unified convergence bound for conjugate gradient and accelerated gradient
Sahar Karimi ; Stephen A. Vavasis ;
Date 2 May 2016
AbstractNesterov’s accelerated gradient method for minimizing a smooth strongly convex function $f$ is known to reduce $f(x_k)-f(x^*)$ by a factor of $epsin(0,1)$ after $kge O(sqrt{L/ell}log(1/eps))$ iterations, where $ell,L$ are the two parameters of smooth strong convexity. Furthermore, it is known that this is the best possible complexity in the function-gradient oracle model of computation. The method of linear conjugate gradients (CG) also satisfies the same complexity bound in the special case of strongly convex quadratic functions, but in this special case it is faster than the accelerated gradient method.
Despite similarities in the algorithms and their asymptotic convergence rates, the conventional analyses of the two methods are nearly disjoint. The purpose of this note is provide a single quantity that decreases on every step at the correct rate for both algorithms. Our unified bound is based on a potential similar to the potential in Nesterov’s original analysis.
As a side benefit of this analysis, we provide a direct proof that conjugate gradient converges in $O(sqrt{L/ell}log(1/eps))$ iterations. In contrast, the traditional indirect proof first establishes this result for the Chebyshev algorithm, and then relies on optimality of conjugate gradient to show that its iterates are at least as good as Chebyshev iterates. To the best of our knowledge, ours is the first direct proof of the convergence rate of linear conjugate gradient in the literature.
Source arXiv, 1605.0320
Services Forum | Review | PDF | Favorites   
 
Visitor rating: did you like this article? no 1   2   3   4   5   yes

No review found.
 Did you like this article?

This article or document is ...
important:
of broad interest:
readable:
new:
correct:
Global appreciation:

  Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.






ScienXe.org
» my Online CV
» Free

home  |  contact  |  terms of use  |  sitemap
Copyright © 2005-2025 - Scimetrica