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.
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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 can be classified into six areas: (I) Network Flows and Integer Programming; (II) Combinatorial Algorithms; (III) Math Computing; (IV) VLSI Physical Design; (V) Operations Research; and (VI) Plasticity.
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.
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a concise introduction to linear and integer programming, appropriate for undergraduates, graduates, a short course or book camp, or self-learningTargets not only computer scientists and engineers, but those in management science and ope. Codice articolo 43799567
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 156 pp. Englisch. Codice articolo 9783319239996
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Codice articolo 9783319239996
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Buch. Condizione: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 156 pp. Englisch. Codice articolo 9783319239996
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