Introduction to applied optimization

Urmila Diwekar (auth.)0387766340, 9780387766355, 9780387766348

This text presents amulti-disciplined view of optimization, providing students and researchers with a thorough examination of algorithms, methods, and tools from diverse areas of optimization without introducing excessive theoretical detail. This second edition includes additional topics, including global optimization and a real-world case study using important concepts from each chapter.

Key Features:

Provides well-written self-contained chapters, including problem sets and exercises, making it ideal for the classroom setting;

Introduces applied optimization to the hazardous waste blending problem;

Explores linear programming, nonlinear programming, discrete optimization, global optimization, optimization under uncertainty, multi-objective optimization, optimal control and stochastic optimal control;

Includes an extensive bibliography at the end of each chapter and an index;

GAMS files of case studies for Chapters 2, 3, 4, 5, and 7 are linked to http://www.springer.com/math/book/978-0-387-76634-8;

Solutions manual available upon adoptions.

Introduction to Applied Optimization is intended for advanced undergraduate and graduate students and will benefit scientists from diverse areas, including engineers.

Table of contents :
Front Matter….Pages 1-20
Introduction….Pages 1-10
Linear Programming….Pages 1-29
Nonlinear Programming….Pages 1-36
Discrete Optimization….Pages 1-48
Optimization Under Uncertainty….Pages 1-54
Multiobjective Optimization….Pages 1-36
Optimal Control and Dynamic Optimization….Pages 1-63
Back Matter….Pages 1-13