Parallel algorithms for computing all possible subset regression models using the QR decomposition

Authors:
Cristian Gatu;Erricos J. Kontoghiorghes
Affiliations:
Institut d'informatique, Université de Neuchâtel, Émile-Argand 11, Case Postale 2, CH-2007 Neuchâtel, Switzerland;Institut d'informatique, Université de Neuchâtel, Émile-Argand 11, Case Postale 2, CH-2007 Neuchâtel, Switzerland
Venue:
Parallel Computing - Special issue: Parallel computing in numerical optimization
Year:
2003

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Abstract

Efficient parallel algorithms for computing all possible subset regression models are proposed. The algorithms are based on the dropping columns method that generates a regression tree. The properties of the tree are exploited in order to provide an efficient load balancing which results in no inter-processor communication. Theoretical measures of complexity suggest linear speedup. The parallel algorithms are extended to deal with the general linear and seemingly unrelated regression models. The case where new variables are added to the regression model is also considered. Experimental results on a shared memory machine are presented and analyzed.