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

Download or Purchase

We have made a PDF version freely downloadable in both a light and dark version.

Light Dark

The print version can be purchased through Cambridge University Press (use code EDO2021 for a pre-order discount) or Amazon.

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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, J. R. R. A. and Ning, A., Engineering Design Optimization, Cambridge University Press, 2022.

@book{mdobook,
author = {Martins, Joaquim R. R. A. and Ning, Andrew},
title = {Engineering Design Optimization},
isbn = {9781108833417},
publisher = {Cambridge University Press},
month = {Jan},
year = {2022}
}