Linear and integer programming are fundamental toolkits for data and information science and technology, particularly in the context of today’s megatrends toward statistical optimization, machine learning, and big data analytics. Drawn from over 30 years of classroom teaching and applied research experience, this textbook provides a crisp and practical introduction to the basics of linear and integer programming. The authors’ approach is accessible to students from all fields of engineering, including operations research, statistics, machine learning, control system design, scheduling, formal verification, and computer vision. Readers will learn to cast hard combinatorial problems as mathematical programming optimizations, understand how to achieve formulations where the objective and constraints are linear, choose appropriate solution methods, and interpret results appropriately.

•Provides a concise introduction to linear and integer programming, appropriate for undergraduates, graduates, a short course or boot camp, or self-learning; 

•Targets not only computer scientists and engineers, but those in management science and operations research as well;

•Emphasizes basics and intuitive concepts, and gives corresponding numerical examples;

•Includes exercises to test and reinforce the concepts introduced, along with a website containing additional material matched to the book’s contents.