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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… mais…

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Conjugate Direction Methods in Optimization - M.R. Hestenes
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Conjugate Direction Methods in Optimization - primeira edição

1980, ISBN: 9780387904559

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M.R. Hestenes:
Conjugate Direction Methods in Optimization - encadernada, livro de bolso

1980

ISBN: 0387904557

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

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M.R. Hestenes:
Conjugate Direction Methods in Optimization - encadernada, livro de bolso

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) - encadernada, livro de bolso

ISBN: 9780387904559

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Conjugate Direction Methods in Optimization
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0387904557


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.

Dados detalhados do livro - Conjugate Direction Methods in Optimization


EAN (ISBN-13): 9780387904559
ISBN (ISBN-10): 0387904557
Livro de capa dura
Ano de publicação: 1980
Editor/Editora: Springer-Verlag New York Inc.
325 Páginas
Peso: 0,644 kg
Língua: eng/Englisch

Livro na base de dados desde 2007-04-13T21:06:53-03:00 (Sao Paulo)
Página de detalhes modificada pela última vez em 2024-03-01T06:22:45-03:00 (Sao Paulo)
Número ISBN/EAN: 0387904557

Número ISBN - Ortografia alternativa:
0-387-90455-7, 978-0-387-90455-9
Ortografia alternativa e termos de pesquisa relacionados:
Autor do livro: rudolph, magnus hestenes, hest, sian rees
Título do livro: optimization methods applications, conjugate direction, applications mathematics, magnus

Dados da editora

Autor: M.R. Hestenes
Título: Stochastic Modelling and Applied Probability; Conjugate Direction Methods in Optimization
Editora: Springer; Springer US
325 Páginas
Ano de publicação: 1980-03-18
New York; NY; US
Língua: Inglês
160,49 € (DE)
164,99 € (AT)
177,00 CHF (CH)
Available
X, 325 p.

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

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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