This textbook provides concise coverage of the basics of linear and integer programming which, with megatrends toward optimization, machine learning, big data, etc., are becoming fundamental toolkits for data and information science and technology. The authors approach is accessible to students from almost all fields of engineering, including operations research, statistics, machine learning, control system design, scheduling, formal verification and computer vision. The presentations enables the basis for numerous approaches to solving hard combinatorial optimization problems through randomization and approximation.
Readers will learn to cast various problems that may arise in their research as optimization problems, understand the cases where the optimization problem will be linear, choose appropriate solution methods and interpret results appropriately.
Preliminaries.- Introduction.- Dimension of the Solution Space.- Introduction to the Simplex Method.- Duality and Complementary Slackness.- Revised Simplex Method.- Column Generating Technique.- The Knapsack Problem.- Asymptotic Algorithms.- The World Map of Integer Programs.- Linear and Integer Programming in Practice
T.C. Hu received his B.S. in Engineering from the National Taiwan University in 1953, M.S. in Engineering from the University of Illinois in 1956, and Ph.D in applied Mathematics from Brown University in 1960. From 1960 to 1966, he was with the IBM Research Center. Dr. Hu was appointed Associate Professor in the Dept. of Computer Science at the University of Wisconsin in 1966 and Full Professor in 1968 (also permanent member of the Mathematics Research Center). In 1974, Dr. Hu was appointed Professor (Step IV) in the Department of Applied Physics and Information Science at the University of California, San Diego, and was promoted to Professor (Step VIII) in 1989. His research contributions lƒ6