Algorithm 587: Two Algorithms for the Linearly Constrained Least Squares Problem

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Algorithm 587: Two Algorithms for the Linearly Constrained Least Squares Problem

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ACM Transactions on Mathematical Software (TOMS) archive
Volume 8 , Issue 3 (September 1982) table of contents

Pages: 323 - 333

Year of Publication: 1982

ISSN:0098-3500

Authors

Richard J. Hanson	Numerical Mathematics Division 5642, Sandia National Laboratories, Albuquerque, NM
Karen H. Haskell	Applied Mathematics Division 2646, Sandia National Laboratories, Albuquerque, NM

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 12, Downloads (12 Months): 132, Citation Count: 6

Additional Information:

appendices and supplements references cited by index terms collaborative colleagues

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APPENDICES and SUPPLEMENTS

LSEI and WNNLS (587.gz) (48 KB)

least squares problems with linear equality and/or inequality constraints
Gams: D9b1

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	P. A. Fox , A. P. Hall , N. L. Schryer, The PORT Mathematical Subroutine Library, ACM Transactions on Mathematical Software (TOMS), v.4 n.2, p.104-126, June 1978 [doi>10.1145/355780.355783]
	2	HANSON, R J Constrained Least Squares Curve F~tt~ng to Dtscrete Data Using B-sphnes--A User's Guide Available as Sandia National Laboratories Tech Rep. SAND78-1291, Sandla National Laboratomes, Albuquerque, N Mex, Feb. 1979.
	3	HASKELL, K.H., AND HANSON, R J. An algorithm for linear least squares problems with equahty and nonnegativlty constraints Math Program. 21 (1981), 98-118.
	4	HASKELL, K.H., AND HANSON, R.J Selected algorithms for the linearly constrained least squares problem--A user's guide Tech Rep. SAND78-1290, Sandia National Laboratories, Albuquerque, N.Mex., 1979.
	5	JONES, R.E. SLATEC common mathematical hbrary error handhng package. Tech. Rep. AND78-1189, Sandia National Laboratories, Albuquerque, N Mex., 1978.
	6	LAWSON, C L. The Covar~ance Matrtx for the Solution Vector of an Equahty.Constra~ned Least-Squares Problem. Available as Jet Propulsion Laboratories Tech Memo 33-807, Jet Propulsion Laboratomes, Pasadena, Calif., Dec. 1976.
	7	LAWSON, C L., AND HANSON, R.J. Solving Least Squares Problems Prentice-Hall, Englewood Cliffs, N J, 1974.
	8	C. L. Lawson , R. J. Hanson , D. R. Kincaid , F. T. Krogh, Basic Linear Algebra Subprograms for Fortran Usage, ACM Transactions on Mathematical Software (TOMS), v.5 n.3, p.308-323, Sept. 1979 [doi>10.1145/355841.355847]
	9	WAMPLER, R.H Problems used m testing the efficiency and accuracy of the modified Gram- Schmidt least squares algorithm. NBS Tech. Note 1126, Aug. 1980.

CITED BY 6

	G. Beliakov, Monotone approximation of aggregation operators using least squares splines, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, v.10 n.6, p.659-676, December 2002
	Melanie L. Lenard , Michael Minkoff, Randomly Generated Test Problems for Positive Definite Quadratic Programming, ACM Transactions on Mathematical Software (TOMS), v.10 n.1, p.86-96, March 1984
	Gleb Beliakov, Approximation of membership functions and aggregation operators using splines, Technologies for constructing intelligent systems: tools, Physica-Verlag GmbH, Heidelberg, Germany, 2002
	V. Dadurkevicius, Remark on algorithm 587, ACM Transactions on Mathematical Software (TOMS), v.15 n.3, p.257-261, Sept. 1989
	W. H. Vandevender , K. H. Haskell, The SLATEC mathematical subroutine library, ACM SIGNUM Newsletter, v.17 n.3, p.16-21, September 1982
	Gleb Beliakov, Learning Weights in the Generalized OWA Operators, Fuzzy Optimization and Decision Making, v.4 n.2, p.119-130, April 2005