Introduction to Applied Optimization
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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.
List of FiguresList of Tables1 Introduction 12 Linear Programming 113 Nonlinear Programming 414 Discrete Optimization 775 Optimization Under Uncertainty 1256 Multiobjective Optimization 1797 Optimal Control And Dynamic Optimization 215Appendix 279Index 283
