Borsdorf, Rudiger and Higham, Nicholas J. (2008) A Preconditioned Newton Algorithm for the Nearest Correlation Matrix. IMA Journal of Numerical Analysis, 30 (1). pp. 94107. ISSN 14643642
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Abstract
Various methods have been developed for computing the correlation matrix nearest in the Frobenius norm to a given matrix. We focus on a quadratically convergent Newton algorithm recently derived by Qi and Sun. Various improvements to the efficiency and reliability of the algorithm are introduced. Several of these relate to the linear algebra: the Newton equations are solved by minres instead of the conjugate gradient method, as it more quickly satisfies the inexact Newton condition; we apply a Jacobi preconditioner, which can be computed efficiently even though the coefficient matrix is not explicitly available; an efficient choice of eigensolver is identified; and a final scaling step is introduced to ensure that the returned matrix has unit diagonal. Potential difficulties caused by rounding errors in the Armijo line search are avoided by altering the step selection strategy. These and other improvements lead to a significant speedup over the original algorithm and allow the solution of problems of dimension a few thousand in a few tens of minutes.
Item Type:  Article 

Additional Information:  Advance Access published on June 8, 2009. 
Uncontrolled Keywords:  correlation matrix, positive semidefinite matrix, Newton's method, preconditioning, rounding error, Armijo line search conditions, alternating projections method 
Subjects:  MSC 2010, the AMS's Mathematics Subject Classification > 65 Numerical analysis MSC 2010, the AMS's Mathematics Subject Classification > 90 Operations research, mathematical programming 
Depositing User:  Nick Higham 
Date Deposited:  28 Jan 2010 
Last Modified:  20 Oct 2017 14:12 
URI:  http://eprints.maths.manchester.ac.uk/id/eprint/1201 
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A Preconditioned Newton Algorithm for the Nearest Correlation Matrix. (deposited 21 Apr 2008)
 A Preconditioned Newton Algorithm for the Nearest Correlation Matrix. (deposited 28 Jan 2010) [Currently Displayed]
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