ISBN: 9781441919106
Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attemp… Más…
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ISBN: 9781441919106
Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attemp… Más…
2010, ISBN: 9781441919106
[ED: Softcover], [PU: Springer, Berlin], Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically.… Más…
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ISBN: 9781441919106
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ISBN: 1441919104
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ISBN: 9781441919106
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Detalles del libro - Optimization
EAN (ISBN-13): 9781441919106
ISBN (ISBN-10): 1441919104
Tapa blanda
Año de publicación: 2010
Editorial: Springer-Verlag GmbH
272 Páginas
Peso: 0,415 kg
Idioma: eng/Englisch
Libro en la base de datos desde 2011-02-12T07:05:03-06:00 (Mexico City)
Página de detalles modificada por última vez el 2018-11-09T18:26:04-06:00 (Mexico City)
ISBN/EAN: 1441919104
ISBN - escritura alterna:
1-4419-1910-4, 978-1-4419-1910-6
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: lange
Título del libro: optimization
Datos del la editorial
Autor: Kenneth Lange
Título: Springer Texts in Statistics; Optimization
Editorial: Springer; Springer US
255 Páginas
Año de publicación: 2010-11-29
New York; NY; US
Peso: 0,850 kg
Idioma: Inglés
80,20 € (DE)
82,45 € (AT)
116,61 CHF (CH)
Not available, publisher indicates OP
BC; Previously published in hardcover; Hardcover, Softcover / Mathematik/Sonstiges; Optimierung; Random variable; linear optimization; algorithms; Excel; linear algebra; optimization; Analysis; nonlinear optimization; modeling; Statistical Methods; expectation–maximization algorithm; Stochastic Processes; A; Optimization; Mathematics and Statistics; Statistical Theory and Methods; Operations Research/Decision Theory; Wahrscheinlichkeitsrechnung und Statistik; Unternehmensforschung; Management: Entscheidungstheorie; BB
1 Elementary Optimization.- 2 The Seven C’s of Analysis.- 3 Differentiation.- 4 Karush-Kuhn-Tucker Theory.- 5 Convexity.- 6 The MM Algorithm.- 7 The EM Algorithm.- 8 Newton’s Method.- 9 Conjugate Gradient and Quasi-Newton.- 10 Analysis of Convergence.- 11 Convex Programming.- Appendix: The Normal Distribution.- A.1 Univariate Normal Random Variables.- A.2 Multivariate Normal Random Vectors.- References.This introduction to optimization attempts to strike a balance between presentation of mathematical theory and development of numerical algorithms. Building on students’ skills in calculus and linear algebra, the text provides a rigorous exposition without undue abstraction and can serve as a bridge to more advanced treatises on nonlinear and convex programming. The emphasis on statistical applications will be especially appealing to graduate students of statistics and biostatistics. The intended audience also includes graduate students in applied mathematics, computational biology, computer science, economics, and physics as well as upper division undergraduate majors in mathematics who want to see rigorous mathematics combined with real applications.
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