Engineering Design Optimization

Erscheinungsjahr 2026
ISBN: 978-1-108-83341-7
Verlag: Cambridge University Press

Buch, Englisch, 650 Seiten, Format (B × H): 208 mm x 260 mm, Gewicht: 1599 g

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.

Martins, Joaquim R. R. A.

Ning, Andrew

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau
• Technische Wissenschaften Bauingenieurwesen Bauingenieurwesen
• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Luft- und Raumfahrttechnik, Luftverkehr
• Technische Wissenschaften Technik Allgemein Konstruktionslehre und -technik

Inhaltsverzeichnis

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.

Über Autor(innen)

Ning, Andrew
Andrew Ning is an Associate Professor of Mechanical Engineering at Brigham Young University, and has previously worked at the National Renewable Energy Laboratory (NREL) as a Senior Engineer.

Martins, Joaquim R. R. A.
Joaquim R. R. A. Martins is a Professor of Aerospace Engineering at the University of Michigan. He is a fellow of the American Institute for Aeronautics and Astronautics, and the Royal Aeronautical Society.