Convex optimization
"Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, covering the theory, many applications and examples, and numerical methods. The book begins with the basic elements of convex sets and functions, describes va...
Enregistré dans:
Auteurs principaux : | , |
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Format : | Livre |
Langue : | anglais |
Titre complet : | Convex optimization / Stephen Boyd,... Lieven Vandenberghe,... |
Publié : |
Cambridge :
Cambridge University Press
, copyright 2004 |
Description matérielle : | 1 vol. (XIII-716 p.) |
Sujets : |
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200 | 1 | |a Convex optimization |f Stephen Boyd,... Lieven Vandenberghe,... | |
210 | |a Cambridge |c Cambridge University Press |d copyright 2004 | ||
215 | |a 1 vol. (XIII-716 p.) |c ill. |d 25 cm | ||
305 | |a Autres tirages : 2004 (avec corrections), 2005, 2006 (avec corrections), 2007, 2008 (avec corrections), 2009 (avec corrections), 2011, 2012, 2013, 2014, 2015 | ||
320 | |a Bibliogr. p. [685]-696. Index | ||
330 | |a "Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, covering the theory, many applications and examples, and numerical methods. The book begins with the basic elements of convex sets and functions, describes various classes of convex optimization problems, and then treats duality theory. The second part covers a wide variety of applications, in estimation, approximation, statistics, computational geometry, and other areas. The last part of the book presents numerical methods for convex optimization problems, moving from basic methods for unconstrained problems to interior-point methods. The focus of the book is on recognizing and formulating convex optimization problems, and then solving them efficiently.[...] (source : 4ème de couverture) | ||
359 | 2 | |c 1. Introduction |b I.Theory |c 2. Convex sets |c 3. Convex functions |c 4. Convex optimization problems |c 5. Duality |b II. Applications |c 6. Approximation and fitting |c 7. Statistical estimation |c 8. Geometric problems |b III. Algorithms |c 9. Unconstrained minimization |c 10. Equality constrained minimization |c 11. Interior-point methods |b Appendices |c A. Mathematical background |c B. Problems involving two quadratic functions |c C. Numerical linear algebra background | |
606 | |3 PPN027244067 |a Optimisation mathématique |2 rameau | ||
606 | |3 PPN03155217X |a Fonctions convexes |2 rameau | ||
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