Joaquim R. R. A. Martins

Engineering Design Optimization (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 18. November 2021
• Englisch
• ISBN-13: 9781108988612
• Artikelnr.: 66189412

Autorenporträt

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.

Inhaltsangabe

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.