Ebook: Introduction to applied optimization

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.

The wide scope of optimization mandates extensive interaction between various disciplines in the development of the methods and algorithms, and in their fruitful application to real-world problems.

This book presents a discipline-independent view of optimization, providing opportunities for students to identify and apply algorithms, methods, and tools from the diverse areas of optimization to their own fields without getting into too much detail about the underlying theories. The second edition of this book includes two new chapters: a chapter on global optimization and a real-world case study that uses principles from each chapter.

Key Features: (1) Provides self-contained chapters, including problem sets and exercises; (2) Introduces applied optimization with several unique applications, i.e., hazardous waste blending problem; (3) Explores a number or important methods, i.e., the simplex method, weighting method, constraint method, and goal programming method; (4) Explores several different types of optimization, i.e., discrete, global, multi-objective, and dynamic optimization; (5) Includes an extensive bibliography at the end of each chapter.

This book is intended for a variety of scientists, engineers, researchers, and advanced students interested in applied optimization.