Amazon cover image
Image from Amazon.com
Image from Google Jackets

Optimization / Kenneth Lange.

By: Material type: TextTextLanguage: English Series: Springer texts in statistics ; 95.Publication details: New York : Springer, c2013.Edition: 2nd edDescription: xvii, 529 p. : ill. ; 24 cmISBN:
  • 9781461458371
  • 1461458374
  • 9781461458388
Subject(s): DDC classification:
  • 519.6 22 LAN
Contents:
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 --
Summary: 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 --
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Collection Call number Status Date due Barcode
General Books General Books CUTN Central Library Sciences Non-fiction 519.6 LAN (Browse shelf(Opens below)) Available 38026

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 --

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 --

Includes bibliographical references (p. 499-518) and index.

There are no comments on this title.

to post a comment.

Powered by Koha