This book, "Network Flows. Theory, Algorithms, and Applications" by Ahuja, Magnanti, and Orlin, is a comprehensive resource on network flows in operations research. It provides an integrative view of the theory, algorithms, and applications of network flows, making it suitable for supplementing upper-level undergraduate or graduate courses. The authors adopt a network or graphical viewpoint to make the content more intuitive and accessible. The book covers in-depth treatment of shortest path, maximum flow, and minimum cost flow problems, including new polynomial-time algorithms. It emphasizes powerful algorithmic strategies and analysis tools, such as data scaling, geometric improvement arguments, and potential function arguments. The book also includes descriptions of important data structures like d-heaps, Fibonacci heaps, and dynamic trees. Other important topics in network optimization and practical solution techniques such as Lagrangian relaxation are discussed. Each new topic is introduced with applications, and there is a special chapter devoted to empirical testing of algorithms. The book includes over 150 applications of network flows in various domains, over 800 exercises, and approximately 400 figures. It also provides extensive reference notes and pseudocodes for several algorithms. The book is well-organized and serves as an essential resource for students and researchers in operations research and mathematical programming. The MSC classifications include 90-01, 90B10, 90C35, and 90-02. The book has been cited in 1851 documents and 3 reviews.This book, "Network Flows. Theory, Algorithms, and Applications" by Ahuja, Magnanti, and Orlin, is a comprehensive resource on network flows in operations research. It provides an integrative view of the theory, algorithms, and applications of network flows, making it suitable for supplementing upper-level undergraduate or graduate courses. The authors adopt a network or graphical viewpoint to make the content more intuitive and accessible. The book covers in-depth treatment of shortest path, maximum flow, and minimum cost flow problems, including new polynomial-time algorithms. It emphasizes powerful algorithmic strategies and analysis tools, such as data scaling, geometric improvement arguments, and potential function arguments. The book also includes descriptions of important data structures like d-heaps, Fibonacci heaps, and dynamic trees. Other important topics in network optimization and practical solution techniques such as Lagrangian relaxation are discussed. Each new topic is introduced with applications, and there is a special chapter devoted to empirical testing of algorithms. The book includes over 150 applications of network flows in various domains, over 800 exercises, and approximately 400 figures. It also provides extensive reference notes and pseudocodes for several algorithms. The book is well-organized and serves as an essential resource for students and researchers in operations research and mathematical programming. The MSC classifications include 90-01, 90B10, 90C35, and 90-02. The book has been cited in 1851 documents and 3 reviews.