A comprehensive, practical introduction to optimization updated with new chapters on duality, quadratic programming, and disciplined convex programming. The extensively updated second edition of this popular textbook provides a comprehensive introduction to optimization with a focus on practical algorithms. Mykel Kochenderfer and Tim Wheeler approach 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. Suitable for advanced undergraduates and graduate students in mathematics, statistics, computer science, engineering, and operations research, and as a reference for professionals, the text provides concrete implementations in the Julia programming language. Second edition highlights:

• New chapters on duality, quadratic programming, and disciplined convex programming
• Additional methods covered, from new ways to estimate gradients to multi-fidelity techniques
• Improved accessibility, expanded explanations, updated references, and streamlined algorithms
• More exercises, example applications, and figures

Mykel Kochenderfer is Associate Professor of Aeronautics and Astronautics at Stanford University, where he is the director of the Stanford Intelligent Systems Laboratory (SISL), author of Decision Making Under Uncertainty, and coauthor of Algorithms for Validation, Algorithms for Optimization, and Algorithms for Decision Making, all published by the MIT Press.

Tim Wheeler is a software engineer in the Bay Area, working on autonomy, controls, and decision-making systems. He is coauthor of Algorithms for Decision Making (MIT Press).