This clear, self-contained guide for graduate students introduces a rapidly developing area of mathematical optimization concerned with hierarchical decision-making. Starting from the basics, the book explains key theoretical ideas and shows how they lead to effective state-of-the-art algorithms for solving hierarchical decision-making problems arising in optimization, operations research, economics, engineering, and data science. The book covers mixed-integer bilevel problems and modern solution techniques such as branch-and-bound and branch-and-cut. Numerous pedagogical elements support learning, including more than 40 figures, 50 examples, over 60 exercises, and exam-style questions at the end of most chapters. With more than 220 references, the book also provides a comprehensive overview of the literature, making it a valuable entry point into this mature and increasingly important field.

This clear, self-contained guide for graduate students introduces a rapidly developing area of mathematical optimization concerned with hierarchical decision-making. Starting from the basics, the book explains key theoretical ideas and shows how they lead to effective state-of-the-art algorithms for solving hierarchical decision-making problems arising in optimization, operations research, economics, engineering, and data science. The book covers mixed-integer bilevel problems and modern solution techniques such as branch-and-bound and branch-and-cut. Numerous pedagogical elements support learning, including more than 40 figures, 50 examples, over 60 exercises, and exam-style questions at the end of most chapters. With more than 220 references, the book also provides a comprehensive overview of the literature, making it a valuable entry point into this mature and increasingly important field.