A class of optimized row projection methods for solving large nonsymmetric linear systems

The optimal and the accelerated row projection methods for solving large nonsymmetric linear systems were discussed. These algorithms use a partition strategy into blocks based on sequential estimations of their condition numbers. These algorithms are extremely fast and efficient, but they do not co...

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Autor principal: Scolnik, H.
Otros Autores: Echebest, N., Guardarucci, M.T, Vacchino, M.C
Formato: Capítulo de libro
Lenguaje:Inglés
Publicado: 2002
Acceso en línea:Registro en Scopus
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Registro en la Biblioteca Digital
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030 |a ANMAE 
100 1 |a Scolnik, H. 
245 1 2 |a A class of optimized row projection methods for solving large nonsymmetric linear systems 
260 |c 2002 
270 1 0 |m Scolnik, H.; Departamento de Computación, Fac. de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina; email: hugo@dc.uba.ar 
506 |2 openaire  |e Política editorial 
504 |a Aharoni, R., Censor, Y., Block-iterative projection methods for parallel computation of solutions to convex feasibility problems (1989) Linear Algebra Appl., 120, pp. 165-175 
504 |a Björck, A., (1996) Numerical Methods for Least Squares Problems, , SIAM, Philadelphia, PA 
504 |a Bramley, R., Sameh, A., Row projection methods for large nonsymmetric linear systems (1992) SIAM J. Sci. Statist. Comput., 13, pp. 168-193 
504 |a Censor, Y., Parallel application of block-iterative methods in medical imaging and radiation therapy (1988) Math. Programming, 42, pp. 307-325 
504 |a Censor, Y., Zenios, S.A., (1997) Parallel Optimization: Theory, Algorithms, and Applications, , Oxford University Press, New York 
504 |a Cimmino, G., Calcolo approssimato per le soluzioni dei sistemi di equazioni lineari (1938) Ricerca Sci. II, 9, pp. 326-333 
504 |a Fletcher, R., (1987) Practical Methods of Optimization, 2nd edn., , Wiley, New York 
504 |a García-Palomares, U.M., Projected aggregation methods for solving a linear system of equalities and inequalities (1991) Mathematical Research, 62, pp. 61-75. , J. Guddat, H.Th. Jongen, B. Kummer, F. Nozicka (Eds.), Parametric Optimization and Related Topics II, Akademie Verlag, Berlin 
504 |a García-Palomares, U.M., Parallel projected aggregation methods for solving the convex feasibility problem (1993) SIAM J. Optim., 3, pp. 882-900 
504 |a Gubin, L.G., Polyak, B.T., Raik, E.V., The method of projections for finding the common point of convex sets (1967) USSR Comput. Math. Math. Phys., 7, pp. 1-24 
504 |a Householder, A.S., Bauer, F.L., On certain iterative methods for solving linear systems (1960) Numer. Math., 2, pp. 55-59 
504 |a Kaczmarz, S., Angenäherte auflösung von systemen linearer gleichungen (1937) Bull. Internat. Acad. Polonaise Sci. Lett., 35, pp. 355-357 
504 |a Saad, Y., Schultz, M.H., Conjugate gradient-like algorithms for solving nonsymmetric linear systems (1985) Math. Comp., 44, pp. 417-424 
504 |a Saad, Y., (1990) SPARSKIT: A basic tool kit for sparse matrix computations, , Technical Report 90-20, Research Institute for Advanced Computer Science, NASA Ames Research Center, Moffett Field, CA 
504 |a Scolnik, H.D., New algorithms for solving large sparse systems of linear equations and their application to nonlinear optimization (1997) Investigación Operativa, 7, pp. 103-116 
504 |a Scolnik, H.D., Echebest, N., Guardarucci, M.T., Vacchino, M.C., A class of optimized row projection methods for solving large nonsymmetric linear systems (2000) Notas de Matemática, 74. , Technical Report, Department of Mathematics, University of La Plata, Buenos Aires, Argentina 
504 |a Van der Vorst, H., A fast and smoothly converging variant of Bi-CG for the solution of nonsymmetric linear systems (1992) SIAM J. Sci. Statist. Comput., 13, pp. 631-644 
520 3 |a The optimal and the accelerated row projection methods for solving large nonsymmetric linear systems were discussed. These algorithms use a partition strategy into blocks based on sequential estimations of their condition numbers. These algorithms are extremely fast and efficient, but they do not converge always. A block splitting algorithm which fulfills the conditions based on the sequential estimations of the condition numbers was also discussed. The performance of the projection methods was highly dependent on the way in which the rows of the matrix were split into blocks.  |l eng 
536 |a Detalles de la financiación: Universidad de Buenos Aires, EX.146, 11/X243 
536 |a Detalles de la financiación: ✩Work supported by the universities of Buenos Aires (Project EX.146) and La Plata (Project 11/X243), Argentina. *Corresponding author. E-mail addresses: hugo@dc.uba.ar (H. Scolnik), opti@mate.unlp.edu.ar (N. Echebest, M.T. Guardarucci, M.C. Vacchino). 
593 |a Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina 
593 |a Departamento de Matemática, Universidad de la Plata, Buenos Aires, Argentina 
690 1 0 |a PARALLEL ITERATIVE METHODS 
690 1 0 |a PROJECTED AGGREGATE METHODS 
690 1 0 |a ROW PARTITION STRATEGIES 
690 1 0 |a CONVERGENCE OF NUMERICAL METHODS 
690 1 0 |a HEURISTIC METHODS 
690 1 0 |a LINEAR SYSTEMS 
690 1 0 |a MATRIX ALGEBRA 
690 1 0 |a OPTIMIZATION 
690 1 0 |a PARALLEL ALGORITHMS 
690 1 0 |a PARTIAL DIFFERENTIAL EQUATIONS 
690 1 0 |a QUADRATIC PROGRAMMING 
690 1 0 |a VECTORS 
690 1 0 |a PROJECTED AGGREGATE METHODS (PAM) 
690 1 0 |a ROW PARTITION STRATEGIES 
690 1 0 |a ITERATIVE METHODS 
700 1 |a Echebest, N. 
700 1 |a Guardarucci, M.T. 
700 1 |a Vacchino, M.C. 
773 0 |d 2002  |g v. 41  |h pp. 499-513  |k n. 4  |p Appl Numer Math  |x 01689274  |w (AR-BaUEN)CENRE-3774  |t Applied Numerical Mathematics 
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