http://dx.doi.org/10.1007/s10898-011-9758-2">
 

Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

An alternating variable method for the maximal correlation problem

Language

English

Abstract

The maximal correlation problem (MCP) aiming at optimizing correlations between sets of variables plays an important role in many areas of statistical applications. Up to date, algorithms for the general MCP stop at solutions of the multivariate eigenvalue problem (MEP), which serves only as a necessary condition for the global maxima of the MCP. For statistical applications, the global maximizer is quite desirable. In searching the global solution of the MCP, in this paper, we propose an alternating variable method (AVM), which contains a core engine in seeking a global maximizer. We prove that (i) the algorithm converges globally and monotonically to a solution of the MEP, (ii) any convergent point satisfies a global optimal condition of the MCP, and (iii) whenever the involved matrix A is nonnegative irreducible, it converges globally to the global maximizer. These properties imply that the AVM is an effective approach to obtain a global maximizer of the MCP. Numerical testings are carried out and suggest a superior performance to the others, especially in finding a global solution of the MCP. © 2011 Springer Science+Business Media, LLC.

Keywords

Canonical correlation, Gauss-Seidal method, Global maximizer, Maximal correlation problem, Multivariate eigenvalue problem, Multivariate statistics, Power method

Publication Date

2012

Source Publication Title

Journal of Global Optimization

Volume

54

Issue

1

Start Page

199

End Page

218

Publisher

Springer Verlag

ISSN (print)

09255001

ISSN (electronic)

15732916

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