Optimization-Theory and Practice / Edition 1 available in Hardcover
- Pub. Date:
- Springer New York
Optimization — Theory and Practice offers a modern and well-balanced presentation of various optimization techniques and their applications. The book's clear structure, sound theoretical basics complemented by insightful illustrations and instructive examples, makes it an ideal introductory textbook and provides the reader with a comprehensive foundation in one of the most fascinating and useful branches of mathematics.
Notable features include:
Detailed explanations of theoretic results accompanied by supporting algorithms and exercises, often supplemented by helpful hints or MATLAB®/MAPLE® code fragments;
an overview of the MATLAB® Optimization Toolbox and demonstrations of its uses with selected examples;
accessibility to readers with a knowledge of multi-dimensional calculus, linear algebra, and basic numerical methods.
Written at an introductory level, this book is intended for advanced undergraduates and graduate students, but may also be used as a reference by academics and professionals in mathematics and the applied sciences.
|Publisher:||Springer New York|
|Series:||Springer Undergraduate Texts in Mathematics and Technology|
|Product dimensions:||7.20(w) x 10.00(h) x 1.00(d)|
About the Author
Dr. Dieter Hoffmann is a professor at the University of Konstanz, Germany.
Drs. Forst and Hoffman previouslyco-authored two German language books for Springer-Verlag: Funktionentheorie explore with Maple (2002) and Ordinary Differential Equations (2005).
Table of Contents1. Introduction: Examples of Optimization Problems, Historical
Overview.- 2. Optimality Conditions: Convex Sets, Inequalities, Local
First- and Second-Order Optimality Conditions, Duality.- 3. Unconstrained Optimization Problems: Elementary Search and Localization Methods, Descent Methods with Line Search, Trust Region Methods, Conjugate Gradient Methods, Quasi-Newton Methods.- 4. Linearly Constrained Optimization Problems: Linear and Quadratic Optimization, Projection Methods.- 5. Nonlinearly Constrained Optimization Methods: Penalty Methods, SQP Methods.- 6. Interior-Point Methods for Linear Optimization: The Central Path, Newton's Method for the Primal-Dual System, Path-Following Algorithms, Predictor-Corrector Methods.- 7. Semidefinite Optimization: Selected Special Cases, The S-Procedure, The Function log°det, Path-Following Methods, How to Solve SDO Problems?, Icing on the Cake: Pattern Separation via Ellipsoids.- 8. Global Optimization: Branch and Bound Methods, Cutting Plane Methods.- Appendices:
A Second Look at the Constraint Qualifications, The Fritz John Condition, Optimization Software Tools for Teaching and Learning.- Bibliography.- Index of Symbols.- Subject Index.