TY  - BOOK
AU  - Lange,Kenneth
TI  - Optimization
T2  - Springer texts in statistics
SN  - 9788181285096
AV  - QA402.5 .L34 2004
U1  - 519.6 22
PY  - 2004///
CY  - Los Angles
PB  - Springer
KW  - Mathematical optimization
KW  - Optimisation mathématique
N1  - 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
N2  - "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
ER  -