Publications

https://mdobook.github.io 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. Table of contents: 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