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Optimization / Kenneth Lange

By: Material type: TextTextSeries: Springer texts in statisticsPublication details: Los Angles : Springer, c2004.Description: xiii, 252 pages ; 25 cmISBN:
  • 9788181285096
  • 038720332X
Subject(s): DDC classification:
  • 519.6 22
LOC classification:
  • QA402.5 .L34 2004
Contents:
1. Elementary optimization -- 2. The seven C's of analysis -- 3. Differentiation -- 4. Karush-Kuhn-Tucker theory -- 5. Convexity -- 6. The MM algorithm -- 7. The EM algorithm -- 8. Newton's method -- 9. Conjugate gradient and quasi-Newton -- 10. Analysis of convergence -- 11. Convex programming -- App. The normal distribution
Review: "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 convexity serves as bridge between linear and nonlinear programming and makes it possible to give a modern exposition of linear programming based on the interior point method rather than the simplex method. The emphasis on statistical applications will be especially appealing to graduate students of statistics and biostatisticsThe intended audience also includes graduate students in applied mathematics, computational biology, computer science, economics, and physics as well as upper division undergraduate majors in mathematics who want to see rigorous mathematics combined with real applications."--Jacket
Item type: Books
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Current library Call number Copy number Status Notes Barcode
KIU Western Campus Engineering Library QA402.5 .L34 2004 (Browse shelf(Opens below)) 001 Available Material is available in hard copy KWC19040422
KIU Western Campus Engineering Library QA402.5 .L34 2004 (Browse shelf(Opens below)) 002 Available Material is available in hard copy KWC19040425

Includes bibliographical references (pages 237-245) and index

1. Elementary optimization -- 2. The seven C's of analysis -- 3. Differentiation -- 4. Karush-Kuhn-Tucker theory -- 5. Convexity -- 6. The MM algorithm -- 7. The EM algorithm -- 8. Newton's method -- 9. Conjugate gradient and quasi-Newton -- 10. Analysis of convergence -- 11. Convex programming -- App. The normal distribution

"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 convexity serves as bridge between linear and nonlinear programming and makes it possible to give a modern exposition of linear programming based on the interior point method rather than the simplex method. The emphasis on statistical applications will be especially appealing to graduate students of statistics and biostatistics

The intended audience also includes graduate students in applied mathematics, computational biology, computer science, economics, and physics as well as upper division undergraduate majors in mathematics who want to see rigorous mathematics combined with real applications."--Jacket

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