Engineering Design Optimization
Joaquim R. R. A. Martins
Andrew Ning
A graduate-level textbook covering a range of fundamental to advanced optimization theory and algorithms with practical tips, numerous illustrations, and engineering examples.
Chapters
- Introduction
- A Short History of Optimization
- Numerical Models and Solvers
- Unconstrained Gradient-Based Optimization
- Constrained Gradient-Based Optimization
- Computing Derivatives
- Gradient-Free Optimization
- Discrete Optimization
- Multiobjective Optimization
- Surrogate-Based Optimization
- Convex Optimization
- Optimization Under Uncertainty
- Multidisciplinary Design Optimization
Download or Purchase
We have made a PDF version freely downloadable in both a light and dark version.
The print version can be purchased through Cambridge University Press or Amazon.
Supplementary Resources
The textbook is accompanied by a repository containing code, examples, and data for some of the exercises in the book.
Lectures for some of the content was recorded during a previous year.
Feedback and Citation
We welcome your feedback, comments, and errata at the following email link.
Citations are available below.
Martins, Joaquim R. R. A. and Ning, Andrew. Engineering Design Optimization. Cambridge University Press, Cambridge, 2022. doi:10.1017/9781108980647. URL https://mdobook.github.io
@book{mdobook,
author = {Joaquim R. R. A. Martins and Andrew Ning},
title = {Engineering Design Optimization},
doi = {10.1017/9781108980647},
isbn = {9781108833417},
month = {January},
publisher = {Cambridge University Press},
address = {Cambridge, UK},
url = {https://mdobook.github.io},
year = {2022}
}
