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Conjugate Direction Methods in Optimization
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ISBN: 9780387904559

Shortly after the end of World War II high-speed digital computing machines were being developed. It was clear that the mathematical aspects of com­ putation needed to be reexamined in or… Más…

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M.R. Hestenes:

Conjugate Direction Methods in Optimization - Primera edición

1980, ISBN: 9780387904559

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M.R. Hestenes:
Conjugate Direction Methods in Optimization - encuadernado, tapa blanda

1980

ISBN: 0387904557

[EAN: 9780387904559], Neubuch, [PU: Springer New York], GARDENING HORTICULTURE GENERAL MATHEMATICS LINEAR & NONLINEAR PROGRAMMING SCIENCE SYSTEM THEORY MATHEMATICA METHODE DER KONJUGIERTE… Más…

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M.R. Hestenes:
Conjugate Direction Methods in Optimization - encuadernado, tapa blanda

1980, ISBN: 9780387904559

Buch, Hardcover, 1980 ed. [PU: Springer-Verlag New York Inc.], Springer-Verlag New York Inc., 1980

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Hestenes, M.R.:
Conjugate Direction Methods in Optimization: 12 (Stochastic Modelling and Applied Probability, 12) - encuadernado, tapa blanda

ISBN: 9780387904559

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Conjugate Direction Methods in Optimization

Shortly after the end of World War II high-speed digital computing machines were being developed. It was clear that the mathematical aspects of com­ putation needed to be reexamined in order to make efficient use of high-speed digital computers for mathematical computations. Accordingly, under the leadership of Min a Rees, John Curtiss, and others, an Institute for Numerical Analysis was set up at the University of California at Los Angeles under the sponsorship of the National Bureau of Standards. A similar institute was formed at the National Bureau of Standards in Washington, D. C. In 1949 J. Barkeley Rosser became Director of the group at UCLA for a period of two years. During this period we organized a seminar on the study of solu­ tions of simultaneous linear equations and on the determination of eigen­ values. G. Forsythe, W. Karush, C. Lanczos, T. Motzkin, L. J. Paige, and others attended this seminar. We discovered, for example, that even Gaus­ sian elimination was not well understood from a machine point of view and that no effective machine oriented elimination algorithm had been developed. During this period Lanczos developed his three-term relationship and I had the good fortune of suggesting the method of conjugate gradients. We dis­ covered afterward that the basic ideas underlying the two procedures are essentially the same. The concept of conjugacy was not new to me. In a joint paper with G. D.

Detalles del libro - Conjugate Direction Methods in Optimization


EAN (ISBN-13): 9780387904559
ISBN (ISBN-10): 0387904557
Tapa dura
Año de publicación: 1980
Editorial: Springer-Verlag New York Inc.
325 Páginas
Peso: 0,644 kg
Idioma: eng/Englisch

Libro en la base de datos desde 2007-04-14T02:06:53+02:00 (Madrid)
Página de detalles modificada por última vez el 2024-03-01T10:22:45+01:00 (Madrid)
ISBN/EAN: 9780387904559

ISBN - escritura alterna:
0-387-90455-7, 978-0-387-90455-9
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: rudolph, magnus hestenes, hest, sian rees
Título del libro: optimization methods applications, conjugate direction, applications mathematics, magnus


Datos del la editorial

Autor: M.R. Hestenes
Título: Stochastic Modelling and Applied Probability; Conjugate Direction Methods in Optimization
Editorial: Springer; Springer US
325 Páginas
Año de publicación: 1980-03-18
New York; NY; US
Peso: 1,430 kg
Idioma: Inglés
160,49 € (DE)
164,99 € (AT)
177,00 CHF (CH)
POD
X, 325 p.

BB; Systems Theory, Control; Hardcover, Softcover / Mathematik/Sonstiges; Kybernetik und Systemtheorie; Verstehen; Mathematica; Methode der konjugierten Richtungen; algorithms; numerical analysis; optimization; Calculus of Variations and Optimal Control; Optimization; Systems Theory, Control; Calculus of Variations and Optimization; Optimierung; BC; EA

I Newton’s Method and the Gradient Method.- 1 Introduction.- 2 Fundamental Concepts.- 3 Iterative Methods for Solving g(x) = 0.- 4 Convergence Theorems.- 5 Minimization of Functions by Newton’s Method.- 6 Gradient Methods—The Quadratic Case.- 7 General Descent Methods.- 8 Iterative Methods for Solving Linear Equations.- 9 Constrained Minima.- II Conjugate Direction Methods.- 1 Introduction.- 2 Quadratic Functions on En.- 3 Basic Properties of Quadratic Functions.- 4 Minimization of a Quadratic Function F on k-Planes.- 5 Method of Conjugate Directions (CD-Method).- 6 Method of Conjugate Gradients (CG-Algorithm).- 7 Gradient PARTAN.- 8 CG-Algorithms for Nonquadratic Functions.- 9 Numerical Examples.- 10 Least Square Solutions.- III Conjugate Gram-Schmidt Processes.- 1 Introduction.- 2 A Conjugate Gram-Schmidt Process.- 3 CGS-CG-Algorithms.- 4 A Connection of CGS-Algorithms with Gaussian Elimination.- 5 Method of Parallel Displacements.- 6 Methods of Parallel Planes (PARP).- 7 Modifications of Parallel Displacements Algorithms.- 8 CGS-Algorithms for Nonquadratic Functions.- 9 CGS-CG-Routines for Nonquadratic Functions.- 10 Gauss-Seidel CGS-Routines.- 11 The Case of Nonnegative Components.- 12 General Linear Inequality Constraints.- IV Conjugate Gradient Algorithms.- 1 Introduction.- 2 Conjugate Gradient Algorithms.- 3 The Normalized CG-Algorithm.- 4 Termination.- 5 Clustered Eigenvalues.- 6 Nonnegative Hessians.- 7 A Planar CG-Algorithm.- 8 Justification of the Planar CG-Algorithm.- 9 Modifications of the CG-Algorithm.- 10 Two Examples.- 11 Connections between Generalized CG-Algorithms and Stadard CG- and CD-Algorithm.- 12 Least Square Solutions.- 13 Variable Metric Algorithms.- 14 A Planar CG-Algorithm for Nonquadratic Functions.- References.

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