Parallel algorithms for downdating the least-squares estimator of the regression model

Petko Yanev () and Erricos John Kontoghirghes
Additional contact information
Petko Yanev: Informatics and Telematics CNR, Pisa
Erricos John Kontoghirghes: University of Cyprus
Authors registered in the RePEc Author Service: Erricos John Kontoghiorghes

No 288, Computing in Economics and Finance 2006 from Society for Computational Economics

Abstract: Computationally efficient parallel algorithms for downdating the least-squares estimator of the ordinary linear model (OLM) are proposed. The algorithms are block versions of sequential Givens strategies and efficiently exploit the triangular structure of the matrices. The first strategy utilizes the orthogonal matrix which is derived from the QR decomposition of the initial data matrix. The orthogonal matrix is updated and explicitely computed. The second approach is based on hyperbolic transformations. This is equivalent to update the model with the imaginary data that is to be deleted. An efficient distribution of matrices over the processors is proposed. Furthermore, the new algorithms do not require any inter-processor communication. The theoretical complexities are derived and experimental results are presented and analyzed. The parallel strategies are scalable and highly efficient for large-scale downdating least-squares problems.

Date: 2006-07-04
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sce:scecfa:288

Access Statistics for this paper

More papers in Computing in Economics and Finance 2006 from Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().