Optimization
 Responsibility
 Kenneth Lange.
 Language
 English.
 Edition
 2nd ed.
 Imprint
 New York : Springer, c2013.
 Physical description
 xvii, 529 p. : ill. ; 24 cm.
 Series
 Springer texts in statistics ; 95.
Access
Available online
 dx.doi.org SpringerLink
Math & Statistics Library
Stacks
Call number  Status 

QA402.5 .L34 2013  Unknown 
More options
Creators/Contributors
 Author/Creator
 Lange, Kenneth.
Contents/Summary
 Bibliography
 Includes bibliographical references (p. 499518) and index.
 Contents

 Elementary Optimization. The Seven C's of Analysis. The Gauge Integral. Differentiation. KarushKuhnTucker Theory. Convexity. Block Relaxation. The MM Algorithm. The EM Algorithm. Newton's Method and Scoring. Conjugate Gradient and QuasiNewton. Analysis of Convergence. Penalty and Barrier Methods. Convex Calculus. Feasibility and Duality. Convex Minimization Algorithms. The Calculus of Variations. Appendix: Mathematical Notes. References. Index.
 (source: Nielsen Book Data)
 Publisher's Summary
 Finitedimensional 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 finitedimensional 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: Nielsen Book Data)
Subjects
 Subject
 Mathematical optimization.
Bibliographic information
 Publication date
 2013
 Series
 Springer texts in statistics ; 95
 ISBN
 9781461458371 (hbk.)
 1461458374 (hbk.)
 9781461458388 (ebk.)
 1461458382 (ebk.)