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Optimization - Kenneth Lange
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[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… Plus…

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Optimization Kenneth Lange Author - nouveau livre

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… Plus…

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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 - edition reliée, livre de poche

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.… Plus…

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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 - edition reliée, livre de poche

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.… Plus…

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Optimization
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Optimization - nouveau livre

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… Plus…

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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.

Informations détaillées sur le livre - Optimization Kenneth Lange Author


EAN (ISBN-13): 9781461458371
ISBN (ISBN-10): 1461458374
Version reliée
Livre de poche
Date de parution: 2013
Editeur: Springer New York Core >2 >T
548 Pages
Poids: 0,971 kg
Langue: Englisch

Livre dans la base de données depuis 2007-10-05T08:09:17+02:00 (Paris)
Page de détail modifiée en dernier sur 2024-03-08T13:37:06+01:00 (Paris)
ISBN/EAN: 9781461458371

ISBN - Autres types d'écriture:
1-4614-5837-4, 978-1-4614-5837-1
Autres types d'écriture et termes associés:
Auteur du livre: lange, kenneth
Titre du livre: optimization, springer texts


Données de l'éditeur

Auteur: Kenneth Lange
Titre: Springer Texts in Statistics; Optimization
Editeur: Springer; Springer US
529 Pages
Date de parution: 2013-03-19
New York; NY; US
Imprimé / Fabriqué en
Poids: 9,398 kg
Langue: Anglais
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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