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A majorization algorithm for constrained correlation matrix approximation

✍ Scribed by Dan Simon; Jeff Abell

Publisher: Elsevier Science
Year: 2010
Tongue: English
Weight: 295 KB
Volume: 432
Category: Article
ISSN: 0024-3795
DOI: 10.1016/j.laa.2009.10.025

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✦ Synopsis

We desire to find a correlation matrix R of a given rank that is as close as possible to an input matrix R, subject to the constraint that specified elements in R must be zero. Our optimality criterion is the weighted Frobenius norm of the approximation error, and we use a constrained majorization algorithm to solve the problem. Although many correlation matrix approximation approaches have been proposed, this specific problem, with the rank specification and the R ij = 0 constraints, has not been studied until now. We discuss solution feasibility, convergence, and computational effort. We also present several examples.

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