Fast Combinatorial Algorithm for the Solution of Linearly Constrained Least Squares Problems
Abstract
A fast combinatorial algorithm can significantly reduce the computational burden when solving general equality and inequality constrained least squares problems with large numbers of observation vectors. The combinatorial algorithm provides a mathematically rigorous solution and operates at great speed by reorganizing the calculations to take advantage of the combinatorial nature of the problems to be solved. The combinatorial algorithm exploits the structure that exists in largescale problems in order to minimize the number of arithmetic operations required to obtain a solution.
 Inventors:

 Middletown, DE
 Albuquerque, NM
 Issue Date:
 Research Org.:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 953741
 Patent Number(s):
 7451173
 Application Number:
 uS patent application 10/938,444
 Assignee:
 Sandia Corporation (Albuquerque, NM)
 Patent Classifications (CPCs):

G  PHYSICS G06  COMPUTING G06F  ELECTRIC DIGITAL DATA PROCESSING
 DOE Contract Number:
 AC0494AL85000
 Resource Type:
 Patent
 Country of Publication:
 United States
 Language:
 English
Citation Formats
Van Benthem, Mark H, and Keenan, Michael R. Fast Combinatorial Algorithm for the Solution of Linearly Constrained Least Squares Problems. United States: N. p., 2008.
Web.
Van Benthem, Mark H, & Keenan, Michael R. Fast Combinatorial Algorithm for the Solution of Linearly Constrained Least Squares Problems. United States.
Van Benthem, Mark H, and Keenan, Michael R. Tue .
"Fast Combinatorial Algorithm for the Solution of Linearly Constrained Least Squares Problems". United States. https://www.osti.gov/servlets/purl/953741.
@article{osti_953741,
title = {Fast Combinatorial Algorithm for the Solution of Linearly Constrained Least Squares Problems},
author = {Van Benthem, Mark H and Keenan, Michael R},
abstractNote = {A fast combinatorial algorithm can significantly reduce the computational burden when solving general equality and inequality constrained least squares problems with large numbers of observation vectors. The combinatorial algorithm provides a mathematically rigorous solution and operates at great speed by reorganizing the calculations to take advantage of the combinatorial nature of the problems to be solved. The combinatorial algorithm exploits the structure that exists in largescale problems in order to minimize the number of arithmetic operations required to obtain a solution.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2008},
month = {11}
}
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Works referenced in this record:
Application of equality constraints on variables during alternating least squares procedures
journal, January 2002
 Van Benthem, Mark H.; Keenan, Michael R.; Haaland, David M.
 Journal of Chemometrics, Vol. 16, Issue 12
Rapid Analysis of Raman Image Data Using TwoWay Multivariate Curve Resolution
journal, June 1998
 Andrew, Jeremy J.; Hancewicz, Thomas M.
 Applied Spectroscopy, Vol. 52, Issue 6
Advantages of Soft versus Hard Constraints in SelfModeling Curve Resolution Problems. Alternating Least Squares with Penalty Functions
journal, August 2003
 Gemperline, Paul J.; Cash, Eric
 Analytical Chemistry, Vol. 75, Issue 16
Simultaneous analysis of several spectroscopic titrations with selfmodelling curve resolution
journal, March 1993
 Tauler, Romà; IzquierdoRidorsa, A.; Casassas, E.
 Chemometrics and Intelligent Laboratory Systems, Vol. 18, Issue 3