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.
Rezensionen / Stimmen
From the reviews:
"The book is well written and the presentation is rigorous and self-contained. The book will be of interest to researchers in various fields as well as undergraduate and graduate students in engineering sciences, management science, and decision science." (I. M. Stancu-Minasian, Zentralblatt MATH, Vol. 1043 (18), 2004)
Reihe
Sprache
Verlagsort
Verlagsgruppe
Illustrationen
60
60 s/w Abbildungen
XV, 335 p. 60 illus.
Dateigröße
ISBN-13
978-1-4757-3745-5 (9781475737455)
DOI
10.1007/978-1-4757-3745-5
Schweitzer Klassifikation
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.
