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Optimization - Kenneth Lange
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Optimization - nuovo libro

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… Altro …

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Optimization Kenneth Lange Author
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Optimization Kenneth Lange Author - nuovo libro

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… Altro …

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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 - copertina rigida, flessible

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.… Altro …

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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 - copertina rigida, flessible

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.… Altro …

Costi di spedizione:Versandkostenfrei, Versand nach Deutschland. (EUR 0.00) preigu
5
Optimization
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Optimization - nuovo libro

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… Altro …

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Dettagli del libro
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.

Informazioni dettagliate del libro - Optimization Kenneth Lange Author


EAN (ISBN-13): 9781461458371
ISBN (ISBN-10): 1461458374
Copertina rigida
Copertina flessibile
Anno di pubblicazione: 2013
Editore: Springer New York Core >2 >T
548 Pagine
Peso: 0,971 kg
Lingua: Englisch

Libro nella banca dati dal 2007-10-05T08:09:17+02:00 (Zurich)
Pagina di dettaglio ultima modifica in 2024-03-08T13:37:06+01:00 (Zurich)
ISBN/EAN: 9781461458371

ISBN - Stili di scrittura alternativi:
1-4614-5837-4, 978-1-4614-5837-1
Stili di scrittura alternativi e concetti di ricerca simili:
Autore del libro : lange, kenneth
Titolo del libro: optimization, springer texts


Dati dell'editore

Autore: Kenneth Lange
Titolo: Springer Texts in Statistics; Optimization
Editore: Springer; Springer US
529 Pagine
Anno di pubblicazione: 2013-03-19
New York; NY; US
Stampato / Fatto in
Peso: 9,398 kg
Lingua: Inglese
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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