Higham, Nicholas J. (1990) Analysis of the Cholesky Decomposition of a Semidefinite Matrix. In: Reliable Numerical Computation. Oxford University Press, Oxford, UK, pp. 161185. ISBN 0198535643
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Abstract
Perturbation theory is developed for the Cholesky decomposition of an $n \times n$ symmetric positive semidefinite matrix $A$ of rank~$r$. The matrix $W=\All^{1}\A{12}$ is found to play a key role in the perturbation bounds, where $\All$ and $\A{12}$ are $r \times r$ and $r \times (nr)$ submatrices of $A$ respectively. A backward error analysis is given; it shows that the computed Cholesky factors are the exact ones of a matrix whose distance from $A$ is bounded by $4r(r+1)\bigl(\norm{W}+1\bigr)^2u\norm{A}+O(u^2)$, where $u$ is the unit roundoff. For the complete pivoting strategy it is shown that $\norm{W}^2 \le {1 \over 3}(nr)(4^r 1)$, and empirical evidence that $\norm{W}$ is usually small is presented. The overall conclusion is that the Cholesky algorithm with complete pivoting is stable for semidefinite matrices. Similar perturbation results are derived for the QR decomposition with column pivoting and for the LU decomposition with complete pivoting. The results give new insight into the reliability of these decompositions in rank estimation.
Item Type:  Book Section 

Uncontrolled Keywords:  Cholesky decomposition, positive semidefinite matrix, perturbation theory, backward error analysis, QR factorization, rank estimation, LINPACK. 
Subjects:  MSC 2010, the AMS's Mathematics Subject Classification > 15 Linear and multilinear algebra; matrix theory MSC 2010, the AMS's Mathematics Subject Classification > 65 Numerical analysis 
Depositing User:  Nick Higham 
Date Deposited:  19 Nov 2008 
Last Modified:  20 Oct 2017 14:12 
URI:  https://eprints.maths.manchester.ac.uk/id/eprint/1193 
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Analysis of the Cholesky Decomposition of a Semidefinite Matrix. (deposited 26 May 2008)
 Analysis of the Cholesky Decomposition of a Semidefinite Matrix. (deposited 19 Nov 2008) [Currently Displayed]
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