An alternating variable method for the maximal correlation problem
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. Copyright Springer Science+Business Media, LLC. 2012
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 54 (2012)
Issue (Month): 1 (September)
|Contact details of provider:|| Web page: http://www.springer.com/business/operations+research/journal/10898|
|Order Information:||Web: http://link.springer.de/orders.htm|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Mohamed Hanafi & Jos Berge, 2003. "Global optimality of the successive Maxbet algorithm," Psychometrika, Springer, vol. 68(1), pages 97-103, March.
- Paul Horst, 1961. "Relations amongm sets of measures," Psychometrika, Springer, vol. 26(2), pages 129-149, June.
- John Geer, 1984. "Linear relations amongk sets of variables," Psychometrika, Springer, vol. 49(1), pages 79-94, March.
When requesting a correction, please mention this item's handle: RePEc:spr:jglopt:v:54:y:2012:i:1:p:199-218. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)or (Christopher F Baum)
If references are entirely missing, you can add them using this form.