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Informationen zum Autor Dr Achille Messac received his B.S., M.S., and Ph.D. from MIT in Aerospace Engineering. Dr Messac is a Fellow of the American Institute of Aeronautics and Astronautics (AIAA) and the American Society of Mechanical Engineers. He has authored or co-authored over 70 journal and 130 conference articles, chaired several international conferences, delivered several keynote addresses, and received the prestigious AIAA Multidisciplinary Design Optimization Award. He has taught or advised undergraduate and graduate students in the areas of design and optimization for over three decades at Rensselaer Polytechnic Institute, MIT, Syracuse University, Mississippi State and Northeastern University. Klappentext This textbook is designed for students and industry practitioners for a first course in optimization integrating MATLAB(R) software. Zusammenfassung This textbook is designed for undergraduate and graduate students and practitioners interested in learning optimization. The presentation is enriched with a robust set of real-world examples and practical exercises. Using MATLAB® software is encouraged throughout. The instructor is supported by a complete solutions manual and PowerPoint slides with animations. Inhaltsverzeichnis Part I. Helpful Preliminaries: 1. MATLAB® as a computational tool; 2. Mathematical preliminaries; Part II. Using Optimization - the Road Map: 3. Welcome to the fascinating world of optimization; 4. Analysis, design, optimization, and modeling; 5. Introducing linear and nonlinear programming; Part III. Using Optimization - Practical Essentials: 6. Multiobjective optimization; 7. Numerical essentials; 8. Global optimization basics; 9. Discrete optimization basics; 10. Practicing optimization - larger examples; Part IV. Going Deeper: Inside the Codes and Theoretical Aspects: 11. Linear programming; 12. Nonlinear programming with no constraints; 13. Nonlinear programming with constraints; Part V. More Advanced Topics in Optimization: 14. Discrete optimization; 15. Modeling complex systems: surrogate modeling and design space reduction; 16. Design optimization under uncertainty; 17. Methods for Pareto frontier generation/representation; 18. Physical programming for multiobjective optimization; 19. Evolutionary algorithms....
