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Precise Solutions for Overdetermined Least Squares Problems

by: James W. Demmel, Yozo Hida, Xiaoye S. Li, E. Jason Riedy, Meghana Vishvanath, and David Vu

Stanford, CA: (March 2007) .

Resources (URL, PDF, PS...)

URL:	http://purl.oclc.org/NET/jason-riedy/resume/material/bascd2007-poster.pdf

Linear least squares LLS fitting is the most widely used data modeling technique and is included in almost every data analysis system e.g. spreadsheets. These software systems often give no feedback on the conditioning of the LLS problem or the floating-point calculation errors present in the solution. With limited use of extra precision, we can eliminate these concerns for all but the most ill-conditioned LLS problems. Our algorithm provides either a solution and residual with relatively tiny error or a notice that the LLS problem is too ill-conditioned.

BibTeX record

@misc{bascd2007-poster,
  abstract = {Linear least squares (LLS) fitting is the most
                  widely used data modeling technique and is included
                  in almost every data analysis system
                  (e.g. spreadsheets). These software systems often
                  give no feedback on the conditioning of the LLS
                  problem or the floating-point calculation errors
                  present in the solution.  With limited use of extra
                  precision, we can eliminate these concerns for all
                  but the most ill-conditioned LLS problems. Our
                  algorithm provides either a solution and residual
                  with relatively tiny error or a notice that the LLS
                  problem is too ill-conditioned.},
  added-at = {2007-10-09T07:04:05.000+0200},
  address = {Stanford, CA},
  author = {Demmel, James W. and Hida, Yozo and Li, Xiaoye S. and Riedy, E. Jason and Vishvanath, Meghana and Vu, David},
  biburl = {http://www.bibsonomy.org/bibtex/28d705a42a836ebf916e911089204dec7/ejr},
  ejr-withauthor = {James W. Demmel and Yozo Hida and Xiaoye S. Li and Meghana Vishvanath and David Vu},
  howpublished = {Stanford 50 -- Eighth Bay Area Scientific Computing Day},
  interhash = {e5ce1c9b69b141fab63cd13fc88b3107},
  intrahash = {8d705a42a836ebf916e911089204dec7},
  keywords = {bascd leastsquares},
  month = {March},
  role = {poster},
  timestamp = {2007-10-09T07:04:05.000+0200},
  title = {Precise Solutions for Overdetermined Least Squares Problems},
  url = {http://purl.oclc.org/NET/jason-riedy/resume/material/bascd2007-poster.pdf},
  year = 2007
}

Endnote record

%0 Generic
%1 bascd2007-poster
%A Demmel, James W.
%A Hida, Yozo
%A Li, Xiaoye S.
%A Riedy, E. Jason
%A Vishvanath, Meghana
%A Vu, David
%C Stanford, CA
%D 2007
%K 
%T Precise Solutions for Overdetermined Least Squares Problems
%U http://purl.oclc.org/NET/jason-riedy/resume/material/bascd2007-poster.pdf
%X Linear least squares (LLS) fitting is the most
                  widely used data modeling technique and is included
                  in almost every data analysis system
                  (e.g. spreadsheets). These software systems often
                  give no feedback on the conditioning of the LLS
                  problem or the floating-point calculation errors
                  present in the solution.  With limited use of extra
                  precision, we can eliminate these concerns for all
                  but the most ill-conditioned LLS problems. Our
                  algorithm provides either a solution and residual
                  with relatively tiny error or a notice that the LLS
                  problem is too ill-conditioned.

BibSonomy :: publication ::

Precise Solutions for Overdetermined Least Squares Problems

Resources (URL, PDF, PS...)

Abstract

BibTeX record

Endnote record