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Incomplete Cholesky factorization

Known as: Cholesky, Incomplete Cholesky 
In numerical analysis, an incomplete Cholesky factorization of a symmetric positive definite matrix is a sparse approximation of the Cholesky… 
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Papers overview

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Highly Cited
2017
Highly Cited
2017
Dense kernel matrices Θ∈R^(N×N) obtained from point evaluations of a covariance function G at locations {x_i}1≤i≤N arise in… 
Highly Cited
2005
Highly Cited
2005
Low-rank matrix decompositions are essential tools in the application of kernel methods to large-scale learning problems. These… 
Highly Cited
2004
Highly Cited
2004
kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 ob ject model… 
Review
2001
Highly Cited
1999
Highly Cited
1999
We propose an incomplete Cholesky factorization for the solution of large-scale trust region subproblems and positive definite… 
Highly Cited
1998
Highly Cited
1998
In this paper, we present two very efficient and accurate algorithms for computing optical flow. The first is a modified gradient… 
Review
1997
Review
1997
  • A. Greenbaum
  • 1997
  • Corpus ID: 117817432
List of Algorithms Preface 1. Introduction. Brief Overview of the State of the Art Notation Review of Relevant Linear Algebra… 
Highly Cited
1995
Highly Cited
1995
Incomplete factorization has been shown to be a good preconditioner for the conjugate gradient method on a wide variety of… 
Highly Cited
1980
Highly Cited
1980
This paper describes a technique for solving the large sparse symmetric linear systems that arise from the application of finite…