Optimization /
Lange, Kenneth.
Optimization / Kenneth Lange - Los Angles : Springer, c2004. - xiii, 252 pages ; 25 cm - Springer texts in statistics . - Springer texts in statistics .
Includes bibliographical references (pages 237-245) and index
Elementary optimization -- The seven C's of analysis -- Differentiation -- Karush-Kuhn-Tucker theory -- Convexity -- The MM algorithm -- The EM algorithm -- Newton's method -- Conjugate gradient and quasi-Newton -- Analysis of convergence -- Convex programming -- The normal distribution 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. App.
"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
9788181285096 038720332X
GBA451888 bnb
Mathematical optimization
Optimisation mathématique
QA402.5 / .L34 2004
519.6
Optimization / Kenneth Lange - Los Angles : Springer, c2004. - xiii, 252 pages ; 25 cm - Springer texts in statistics . - Springer texts in statistics .
Includes bibliographical references (pages 237-245) and index
Elementary optimization -- The seven C's of analysis -- Differentiation -- Karush-Kuhn-Tucker theory -- Convexity -- The MM algorithm -- The EM algorithm -- Newton's method -- Conjugate gradient and quasi-Newton -- Analysis of convergence -- Convex programming -- The normal distribution 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. App.
"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
9788181285096 038720332X
GBA451888 bnb
Mathematical optimization
Optimisation mathématique
QA402.5 / .L34 2004
519.6