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.

  Inhalt

  Foreword. Acknowledgements.
  1: Introduction.
  1.1. Problem Formulation: A Cautionary Note. 1.2. Degrees of Freedom Analysis. 1.3. Objective Function, Constraints, and Feasible Region. 1.4. Numerical Optimization. 1.5. Types of Optimization Problems. 1.6. Summary.
  2: Linear Programming.
  2.1. The Simplex Method. 2.2. Infeasible Solution. 2.3. Unbounded Solution. 2.4. Multiple Solutions. 2.5. Sensitivity Analysis. 2.6. Other Methods. 2.7. Hazardous Waste Blending Problem as an LP. 2.8. Summary.
  3: Nonlinear Programming.
  3.1. Convex and Concave Functions. 3.2. Unconstrained NLP. 3.3. Necessary and Sufficient Conditions, and Constrained NLP. 3.4. Sensitivity Analysis. 3.5. Numerical Methods. 3.6. Hazardous Waste Blending: An NLP. 3.7. Summary.
  4: Discrete Optimization.
  4.1. Tree and Network Representation. 4.2. Branch and Bound for IP. 4.3. Numerical Methods for IP, MILP, and MINLP. 4.4. Probabilistic Methods. 4.5. Hazardous Waste Blending: A Combinatorial Problem. 4.6. Summary.
  5: Optimization Under Uncertainty.
  5.1. Types of Problems and Generalized Representation. 5.2. Chance Constrained Programming Method. 5.3. L-shaped Decomposition Method. 5.4. Uncertainty Analysis and Sampling. 5.5. Stochastic Annealing: An Efficient Algorithm for Combinatorial Optimization under Uncertainty. 5.6. Hazardous Waste Blending under Uncertainty. 5.7. Summary.
  6: Multi-objective Optimization.
  6.1. Nondominated Set. 6.2. Solution Methods. 6.3. Hazardous Waste Blending and Value of Research: An MOP. 6.4. Summary.
  7: Optimal control And Dynamic Optimization.
  7.1. Calculus of Variations. 7.2. Maximum Principle. 7.3. Dynamic Programming. 7.4. Stochastic Dynamic Programming. 7.5. Reversal of Blending: Optimizing a Separation Process. 7.6. Summary.
  Appendix A. Appendix B.
  Index.