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.

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In a relatively compact book, Diwekar manages to give detailed explanations of key methods in modern optimisation. These include simulated annealing and genetic algorithms. The former are inspired by ideas in statistical mechanics, and the latter by evolution. Monte Carlo sampling is another important idea well described here. It uses a pseudo-random number generator that approximates a uniform distribution over [0,1] to do probabilistic analysis in the common case when analytic answers are unavailable. The narrative gives the reader an appreciation of what problems these methods can be used against, and also of the computational complexity of each method.