Buch, Englisch, 650 Seiten, Format (B × H): 195 mm x 254 mm, Gewicht: 1547 g
Buch, Englisch, 650 Seiten, Format (B × H): 195 mm x 254 mm, Gewicht: 1547 g
ISBN: 978-1-108-83341-7
Verlag: Cambridge University Press
Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 400 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice. Accompanied online by a solutions manual for instructors and source code for problems, this is ideal for a one- or two-semester graduate course on optimization in aerospace, civil, mechanical, electrical, and chemical engineering departments.
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Verkehrstechnik | Transportgewerbe Luft- und Raumfahrttechnik, Luftverkehr
- Technische Wissenschaften Technik Allgemein Konstruktionslehre und -technik
- Technische Wissenschaften Bauingenieurwesen Bauingenieurwesen
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau
Weitere Infos & Material
1. Introduction; 2. A short history of optimization; 3. Numerical models and solvers; 4. Unconstrained gradient-based optimization; 5. Constrained gradient-based optimization; 6. Computing derivatives; 7. Gradient-free optimization; 8. Discrete optimization; 9. Multiobjective optimization; 10. Surrogate-based optimization; 11. Convex optimization; 12. Optimization under uncertainity; 13. Multidisciplinary design optimization; A. Mathematics background; B. Linear solvers; C. Quasi-Newton methods; D. Test problems.