Extra-Precise Iterative Refinement for Overdetermined Least Squares Problems

Authors:
James Demmel;Yozo Hida;E. Jason Riedy;Xiaoye S. Li
Affiliations:
University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;Lawrence Berkeley National Laboratory
Venue:
ACM Transactions on Mathematical Software (TOMS)
Year:
2009

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Abstract

We present the algorithm, error bounds, and numerical results for extra-precise iterative refinement applied to overdetermined linear least squares (LLS) problems. We apply our linear system refinement algorithm to Björck’s augmented linear system formulation of an LLS problem. Our algorithm reduces the forward normwise and componentwise errors to O(ϵw), where ϵw is the working precision, unless the system is too ill conditioned. In contrast to linear systems, we provide two separate error bounds for the solution x and the residual r. The refinement algorithm requires only limited use of extra precision and adds only O(mn) work to the O(mn2) cost of QR factorization for problems of size m-by-n. The extra precision calculation is facilitated by the new extended-precision BLAS standard in a portable way, and the refinement algorithm will be included in a future release of LAPACK and can be extended to the other types of least squares problems.