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Article
A Majorization Algorithm for Constrained Correlation Matrix Approximation
Linear Algebra and its Applications
  • Daniel J. Simon, General Motors Company
  • Jeff Abell, University of California - Davis
Document Type
Article
Publication Date
2-1-2010
Abstract
We desire to find a correlation matrix of a given rank that is as close as possible to an input matrix R, subject to the constraint that specified elements in 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 constraints, has not been studied until now. We discuss solution feasibility, convergence, and computational effort. We also present several examples.
DOI
10.1016/j.laa.2009.10.025
Version
Postprint
Citation Information
Dan Simon, Jeff Abell. (2010). A majorization algorithm for constrained correlation matrix approximation. Linear Algebra and its Applications, 432(5), 1152-1164, doi: 10.1016/j.laa.2009.10.025.