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...
Enregistré dans:
| 主要作者: | |
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| 格式: | Livre papier |
| 語言: | Anglais |
| 出版: |
New York :
Springer
C 2013.
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| 版: | 2nd edition. |
| 叢編: | Springer texts in statistics
95 |
| 主題: | |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Optimization, by Kenneth Lange., 2nd ed. 2013., New York, NY, Springer New York, Imprint: Springer, 2013, Springer Texts in Statistics, 978-1-461-45838-8 |
| LEADER | 02808nam a22003017a 4500 | ||
|---|---|---|---|
| 001 | 716704 | ||
| 008 | 140213t20132013xxe ||| |||| 00| 0 eng d | ||
| 009 | PPN17630584X | ||
| 020 | |a 9781461458371 (rel.) | ||
| 020 | |a 1461458374 (rel.) | ||
| 020 | |a 9781489992703 (br.) | ||
| 024 | |a 9781461458371 | ||
| 024 | |a 9781489992703 | ||
| 041 | 0 | |a eng | |
| 082 | |a 519.6 | ||
| 100 | 1 | |a Lange, Kenneth, |d 1946- | |
| 245 | 1 | 0 | |a Optimization |c Kenneth Lange. |
| 250 | |a 2nd edition. | ||
| 260 | |a New York : |b Springer. | ||
| 260 | |c C 2013. | ||
| 300 | |a 1 vol. (XVII-529 p.) : |b fig. ; |c 24 cm. | ||
| 490 | 1 | |a Springer texts in statistics |x 1431-875X |v 95 | |
| 504 | |a Bibliogr. p. 499-518. Index | ||
| 505 | 1 | |a 1. Elementary optimization 2. The seven c's of analysis 3. The gauge integral 4. Differentiation 5. Karush-Kuhn-Tucker theory 6. Convexity 7. Block relaxation 8. The MM algorithm 9. The EM algorithm 10. Newton's method and scoring 11. Conjugate gradient and quasi-Newton 12. Analysis of convergence 13. Penalty and barrier methods 14. Convex calculus 15. Feasibility and duality 16. Convex minimization algorithms 17. The calculus of variations Appendix | |
| 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. Source : 4e de couv. | ||
| 650 | |a Optimisation mathématique | ||
| 776 | 0 | |0 169135837 |t Optimization |f by Kenneth Lange. |e 2nd ed. 2013. |c New York, NY |n Springer New York |n Imprint: Springer |d 2013 |s Springer Texts in Statistics |z 978-1-461-45838-8 | |
| 997 | |0 716704 |1 Livre papier |a Ressource papier |c 0/Orléans/ |c 1/Orléans/IDP/ |z Orléans, IDP, 9150 LAN | ||