Introduction to Applied Optimization

Description

Preface -- 1. Introduction -- 2. Linear Programming -- 3. Nonlinear Programming -- 4. Discrete Optimization -- 5. Optimization under Uncertainty -- 6. Multi-objective Optimization -- 7. Optimal Control and Dynamic Optimization -- Index;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 Preface -- 1. Introduction -- 2. Linear Programming -- 3. Nonlinear Programming -- 4. Discrete Optimization -- 5. Optimization under Uncertainty -- 6. Multi-objective Optimization -- 7. Optimal Control and Dynamic Optimization -- Index