Iterative methods for equality-constrained least squares problems

We consider the linear equality-constrained least squares problem (LSE) of minimizing ${\|c - Gx\|}_2 $, subject to the constraint $Ex = p$. A preconditioned conjugate gradient method is applied to the Kuhn-Tucker equations associated with the LSE problem. We show that our method is well suited for...

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Veröffentlicht in:	SIAM J. Sci. Stat. Comput.; (United States) 1988-09, Vol.9 (5), p.892-906
Hauptverfasser:	BARLOW, J. L, NICHOLS, N. K, PLEMMONS, R. J
Format:	Artikel
Sprache:	eng
Schlagworte:	990200 > Mathematics & Computers Algorithms Applied mathematics Applied sciences ARRAY PROCESSORS COMPUTER CALCULATIONS Computer science COMPUTERS CONSTRAINTS CRAY COMPUTERS Exact sciences and technology GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE ITERATIVE METHODS LEAST SQUARE FIT MAXIMUM-LIKELIHOOD FIT Methods NUMERICAL SOLUTION 990210 > Supercomputers-- (1987-1989) Operational research and scientific management Operational research. Management science OPTIMIZATION Optimization. Search problems
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Zusammenfassung:	We consider the linear equality-constrained least squares problem (LSE) of minimizing ${\\|c - Gx\\|}_2 $, subject to the constraint $Ex = p$. A preconditioned conjugate gradient method is applied to the Kuhn-Tucker equations associated with the LSE problem. We show that our method is well suited for structural optimization problems in reliability analysis and optimal design. Numerical tests are performed on an Alliant FX/8 multiprocessor and a Cray-X-MP using some practical structural analysis data.
ISSN:	0196-5204 1064-8275 2168-3417 1095-7197
DOI:	10.1137/0909061