Introduction to Applied Optimization

This text presents a multi-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.

Texte du rabat

• 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.

Contenu

Linear Programming.- Nonlinear Programming.- Discrete Optimization.- Optimization Under Uncertainty.- Multiobjective Optimization.- Optimal Control and Dynamic Optimization.