Endorsements

“The book gives a comprehensive treatment of all the optimization techniques that are useful in solving problems in engineering—a selection that spans much of the whole field of optimization. The exposition is direct and elegant. The book is beautifully designed, with high-quality, always-relevant figures and helpful notes and citations in the margins. A deeply satisfying work!”

Prof. Stephen Wright, University of Wisconsin-Madison

“I read large portions of your book on my way back to Chicago, and I love it.”

Prof. Jorge Nocedal, Northwestern University

Chapters

  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 Uncertainty
  13. Multidisciplinary Design Optimization

Supplementary Resources

The textbook is accompanied by a repository containing code, examples, and data for some of the exercises in the book.

Github Repo

Lectures for some of the content was recorded during a previous year.

YouTube Channel

Feedback and Citation

We welcome your feedback, comments, and errata at the following email link.

Email

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}
}