The mathematics of nonlinear programming
Author(s)
Bibliographic Information
The mathematics of nonlinear programming
（Undergraduate texts in mathematics）
Springer-Verlag, c1988
- : us
- : gw
Available at / 87 libraries
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Research Institute for Economics & Business Administration (RIEB) Library , Kobe University図書
: us519.9-581s081000081073*
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Faculty of Textile Science and Technology Library, Shinshu University図
: gw417:P41:207012810087953
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Note
Includes index
Description and Table of Contents
- Volume
-
: us ISBN 9780387966144
Description
Nonlinear programming provides an excellent opportunity to explore an interesting variety of pure and solidly applicable mathematics, numerical analysis, and computing. This text develops some of the ideas and techniques involved in the optimization methods using calculus, leading to the study of convexity. This is followed by material on basic numerical methods, least squares, the Karush-Kuhn-Tucker theorem, penalty functions, and Lagrange multipliers. The authors have aimed their presentation at the student who has a working knowledge of matrix algebra and advanced calculus, but has had no previous exposure to optimization.
Table of Contents
- Preface
- 1: Unconstrained Optimization via Calculus
- 2: Convex and Convex Functions
- 3: Iterative Methods for Unconstrained Optimization
- 4: Least Squares Optimization
- 5: Convex Programming and the Karush-Kuhn-Tucker Conditions
- 6: Penalty Methods
- 7: Optimization with Equality Constraints
- Index
- Volume
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: gw ISBN 9783540966142
Description
This monograph develops ideas and techniques involved in optimization methods using calculus, leading to the study of convexity. This is followed by discussion of basic numerical methods, least squares, the Karush-Kuhn-Tucker Theorem, penality functions and Lagrange multipliers.
Table of Contents
Unconstrained Optimization via Calculus. Convex Sets and Convex Functions.- Iterative Methods for Unconstrained Optimization.- Least Squares Optimization.- Convex Programming and the Karush-Kuhn-Tucker Conditions.- Penalty Methods.-Optimization with Equality Constraints.
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