Optimization is a foundational topic in mathematics, underpinning nearly all of our modern industrial and technological world. Assuming only basic knowledge of linear algebra and calculus, this book provides a rapid, yet thorough, overview of applied mathematical optimization for advanced undergraduates, beginning graduate students, or practitioners in science and engineering. The text opens with an “Optimization Bootcamp”, introducing methods at a beginning level, before progressing to deep-dives into advanced topics and research-ready methods. The focus throughout is on modern applications of machine learning, inverse problems, and control. Rich pedagogy includes Python code with simple working examples and advanced case studies. Every section is accompanied by YouTube lectures to encourage interaction with the material. Using intuitive explanations, this book makes the material as simple and interesting as possible, while still having the depth, breadth and precision required to empower use in research and real-world applications.

‘Steve Brunton explores optimization with clarity and ambition. Throughout, the book maintains an excellent balance between mathematical insight and practical implementation, with well-chosen examples and Python code that illuminate what is happening beneath the algorithmic surface. This is an accessible text for readers encountering the material for the first time and a valuable reference for researchers wanting to study one of the topics presented in greater depth.’

Richard Murray - Caltech

‘I would strongly recommend Steve Brunton's Optimization Bootcamp to any beginning student of Applied Math, Engineering, or Machine Learning. The book covers many of the most commonly used optimization methods, with practical examples and problems in different fields, from fitting models to data, to designing mechanical structures. Its integrated Python examples send a clear message to the student: This material is meant to be used.’

Stephen Boyd - Stanford University