Conjugate gradient method

Known as: Preconditioned conjugate gradient method, Polak-Ribier, Method of conjugate gradient 
In mathematics, the conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those… (More)
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1963-2018
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Highly Cited
2008
Highly Cited
2008
  • Trond Steihaug Source
  • 2008
Algorithms based on trust regions have been shown to be robust methods for unconstrained optimization problems. All existing… (More)
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Highly Cited
2003
Highly Cited
2003
Many computer graphics applications require high-intensity numerical simulation. We show that such computations can be performed… (More)
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Highly Cited
2001
Highly Cited
2001
Numerical solution of extremely large and ill conditioned eigenvalue problems is attracting a growing attention recently as such… (More)
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Highly Cited
2001
Highly Cited
2001
  • 2001
The advent of electronic computers in the middle of the 20th century stimulated a flurry of activity in developing numerical… (More)
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Highly Cited
2000
Highly Cited
2000
We present a variant of the AINV factorized sparse approximate inverse algorithm which is applicable to any symmetric positive… (More)
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Highly Cited
1999
Highly Cited
1999
An important variation of preconditioned conjugate gradient algorithms is inexact precon-ditioner implemented with inner-outer… (More)
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Highly Cited
1999
Highly Cited
1999
A subspace adaptation of the Coleman-Li trust region and interior method is proposed for solving large-scale bound-constrained… (More)
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Highly Cited
1997
Highly Cited
1997
1 ABSTRACT In this paper we propose a new line search algorithm that ensures global convergence of the Polak-Ribi ere conjugate… (More)
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Highly Cited
1996
Highly Cited
1996
A method for computing a sparse incomplete factorization of the inverse of a symmetric positive definite matrix A is developed… (More)
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Highly Cited
1991
Highly Cited
1991
  • MARTIN FODSLETTE MEILLER
  • 1991
-A supervised learning algorithm (Scaled Conjugate Gradient, SCG) is introduced TIw pelformance of SCG is benchmarked against… (More)
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