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Optimization - Kenneth Lange
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2019, ISBN: 9781461458371

[ED: Buch], [PU: Springer New York], Neuware - Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analyti… mais…

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Optimization Kenneth Lange Author
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Optimization Kenneth Lange Author - nuovo livro

ISBN: 9781461458371

Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attemp… mais…

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Optimization / Kenneth Lange / Buch / Springer Texts in Statistics / HC gerader Rücken kaschiert / XVII / Englisch / 2013 / Springer New York / EAN 9781461458371 - Lange, Kenneth
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Optimization / Kenneth Lange / Buch / Springer Texts in Statistics / HC gerader Rücken kaschiert / XVII / Englisch / 2013 / Springer New York / EAN 9781461458371 - encadernada, livro de bolso

2013

ISBN: 9781461458371

[ED: Gebunden], [PU: Springer New York], Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically.… mais…

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Optimization | Kenneth Lange | Buch | Springer Texts in Statistics | HC gerader Rücken kaschiert | XVII | Englisch | 2013 | Springer New York | EAN 9781461458371 - Lange, Kenneth
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Lange, Kenneth:
Optimization | Kenneth Lange | Buch | Springer Texts in Statistics | HC gerader Rücken kaschiert | XVII | Englisch | 2013 | Springer New York | EAN 9781461458371 - encadernada, livro de bolso

2013, ISBN: 9781461458371

[ED: Gebunden], [PU: Springer New York], Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically.… mais…

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Optimization
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Optimization - nuovo livro

ISBN: 9781461458371

Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attemp… mais…

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Optimization Kenneth Lange Author

Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. 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. Its stress on statistical applications will be especially appealing to graduate students of statistics and biostatistics. The intended audience also includes students in applied mathematics, computational biology, computer science, economics, and physics who want to see rigorous mathematics combined with real applications. In this second edition the emphasis remains on finite-dimensional optimization. New material has been added on the MM algorithm, block descent and ascent, and the calculus of variations. Convex calculus is now treated in much greater depth. Advanced topics such as the Fenchel conjugate, subdifferentials, duality, feasibility, alternating projections, projected gradient methods, exact penalty methods, and Bregman iteration will equip students with the essentials for understanding modern data mining techniques in high dimensions.

Dados detalhados do livro - Optimization Kenneth Lange Author


EAN (ISBN-13): 9781461458371
ISBN (ISBN-10): 1461458374
Livro de capa dura
Livro de bolso
Ano de publicação: 2013
Editor/Editora: Springer New York Core >2 >T
548 Páginas
Peso: 0,971 kg
Língua: Englisch

Livro na base de dados desde 2007-10-05T03:09:17-03:00 (Sao Paulo)
Página de detalhes modificada pela última vez em 2024-03-08T09:37:06-03:00 (Sao Paulo)
Número ISBN/EAN: 9781461458371

Número ISBN - Ortografia alternativa:
1-4614-5837-4, 978-1-4614-5837-1
Ortografia alternativa e termos de pesquisa relacionados:
Autor do livro: lange, kenneth
Título do livro: optimization, springer texts


Dados da editora

Autor: Kenneth Lange
Título: Springer Texts in Statistics; Optimization
Editora: Springer; Springer US
529 Páginas
Ano de publicação: 2013-03-19
New York; NY; US
Impresso / Feito em
Peso: 9,398 kg
Língua: Inglês
171,19 € (DE)
175,99 € (AT)
189,00 CHF (CH)
POD
XVII, 529 p.

BB; Statistical Theory and Methods; Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; Convexity; Differentiation; Gauge Integral; Integration; Optimization; Statistical variance; Optimization; Operations Research/Decision Theory; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Statistical Theory and Methods; Optimization; Operations Research and Decision Theory; Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Optimierung; Unternehmensforschung; Management: Entscheidungstheorie; BB; BC; EA

Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. 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. Its stress on statistical applications will be especially appealing to graduate students of statistics and biostatistics. The intended audience also includes students in applied mathematics, computational biology, computer science, economics, and physics who want to see rigorous mathematics combined with real applications. In this second edition, the emphasis remains on finite-dimensional optimization. New material has been added on the MM algorithm, block descent and ascent, and the calculus of variations. Convex calculus is now treated in much greater depth.  Advanced topics such as the Fenchel conjugate, subdifferentials, duality, feasibility, alternating projections, projected gradient methods, exact penalty methods, and Bregman iteration will equip students with the essentials for understanding modern data mining techniques in high dimensions.

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