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Algorithms for Optimization

Source: https://algorithmsbook.com/optimization/ Second edition releasing January 5, 2027 · First edition, 2019

• Intro
• Download
• Buy
• Outline
• Ancillaries
• Errata

Intro

This book provides a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language.

Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text.

The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.

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The full book is available as a PDF.

Second edition

MIT Press, January 5, 2027 · Preview

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The PDF is shared under a Creative Commons CC‑BY‑NC‑ND license.

The copyright of this book has been licensed exclusively toThe MIT Press. All inquiries regarding rights should be addressed to The MIT Press, Rights and Permissions Department.

Buy

The second edition is available forpre-orderwith a release date of January 5, 2027.

Thefirst editionis available in print now.

Outline

Contents of the second edition.

1. Introduction
2. Derivatives and Gradients
3. Bracketing
4. Local Descent
5. First-Order Methods
6. Second-Order Methods
7. Direct Methods
8. Stochastic Methods
9. Population Methods
10. Constraints
11. Duality
12. Linear Programming
13. Quadratic Programming
14. Disciplined Convex Programming
15. Multiobjective Optimization
16. Sampling Plans
17. Surrogate Models
18. Probabilistic Surrogate Models
19. Surrogate Optimization
20. Optimization under Uncertainty
21. Uncertainty Propagation
22. Discrete Optimization
23. Expression Optimization
24. Multidisciplinary Optimization

Appendices

1. Julia
2. Test Functions
3. Mathematical Concepts

Ancillaries

Supporting material is maintained on GitHub.

• Ancillaries— slides and supplementary material.
• Notebooks— the code blocks from the book.

Errata

Please file issues onGitHubor email the address listed at the bottom of the pages of the PDF. The PDF is kept up to date with any corrections.