Optimization

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. Build...

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書誌詳細
第一著者: Lange, Kenneth, 1946-
フォーマット: Livre numérique
言語:Anglais
出版事項: New York, NY : Springer New York [20..].
Cham : Springer Nature
版:2nd ed. 2013.
シリーズ:Springer Texts in Statistics 95
オンライン・アクセス:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
注記: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Optimization, Kenneth Lange, 2nd edition, 2013, New York, Springer, 1 vol. (XVII-529 p.), Springer texts in statistics, 978-1-461-45837-1
• Optimization, Texte imprimé, 9781461458395
• Optimization, Kenneth Lange, 2nd edition, 2013, New York, Springer, 1 vol. (XVII-529 p.), Springer texts in statistics, 978-1-461-45837-1
• Optimization, Kenneth Lange, 2nd edition, 2013, New York, Springer, 1 vol. (XVII-529 p.), Springer texts in statistics, 978-1-461-45837-1
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505 1 |a Elementary Optimization The Seven C s of Analysis The Gauge Integral Differentiation Karush-Kuhn-Tucker Theory Convexity Block Relaxation The MM Algorithm The EM Algorithm Newton s Method and Scoring Conjugate Gradient and Quasi-Newton Analysis of Convergence Penalty and Barrier Methods Convex Calculus Feasibility and Duality Convex Minimization Algorithms The Calculus of Variations Appendix: Mathematical Notes References Index 
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506 |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 
520 |a 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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