Overview
- Provides an integration of mathematical theory and development of numerical algorithms for applied optimization
- Includes new chapters on calculus of variations, integration, and block relaxation
- Showcases balance between presentation of mathematical theory and development of numerical algorithms
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Texts in Statistics (STS, volume 95)
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Table of contents (17 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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. Â 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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Authors and Affiliations
About the author
Bibliographic Information
Book Title: Optimization
Authors: Kenneth Lange
Series Title: Springer Texts in Statistics
DOI: https://doi.org/10.1007/978-1-4614-5838-8
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media New York 2013
Hardcover ISBN: 978-1-4614-5837-1Published: 19 March 2013
Softcover ISBN: 978-1-4899-9270-3Published: 03 April 2015
eBook ISBN: 978-1-4614-5838-8Published: 19 March 2013
Series ISSN: 1431-875X
Series E-ISSN: 2197-4136
Edition Number: 2
Number of Pages: XVII, 529
Topics: Statistical Theory and Methods, Optimization, Operations Research/Decision Theory, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences