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Linear Optimization and Duality
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Linear Optimization and Duality : A Modern Exposition

Book Details

Format Paperback / Softback
ISBN-10 0367618206
ISBN-13 9780367618209
Publisher Taylor & Francis Ltd
Imprint Chapman & Hall/CRC
Country of Manufacture GB
Country of Publication GB
Publication Date Aug 26th, 2024
Print length 586 Pages
Weight 1,064 grams
Dimensions 25.40 x 17.90 x 3.50 cms
Ksh 11,500.00
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This textbook presents a theoretical treatment of linear programming, network flows and applications, integer programming, and computational complexity. The author includes a rigorous discussion of theory, numerous examples and exercises, and geometric intuitive explanations. He also offers computational tips and interpretation of software input.

Linear Optimization and Dualiyy: A Modern Exposition departs from convention in significant ways. Standard linear programming textbooks present the material in the order in which it was discovered. Duality is treated as a difficult add-on after coverage of formulation, the simplex method, and polyhedral theory. Students end up without knowing duality in their bones.

This text brings in duality in Chapter 1 and carries duality all the way through the exposition. Chapter 1 gives a general definition of duality that shows the dual aspects of a matrix as a column of rows and a row of columns. The proof of weak duality in Chapter 2 is shown via the Lagrangian, which relies on matrix duality. The first three LP formulation examples in Chapter 3 are classic primal-dual pairs including the diet problem and 2-person zero sum games.

For many engineering students, optimization is their first immersion in rigorous mathematics. Conventional texts assume a level of mathematical sophistication they don’t have. This text embeds dozens of reading tips and hundreds of answered questions to guide such students.

Features

  • Emphasis on duality throughout
  • Practical tips for modeling and computation
  • Coverage of computational complexity and data structures
    • Exercises and problems based on the learning theory concept of the zone of proximal
  • development

    • Guidance for the mathematically unsophisticated reader

    About the Author

    Craig A. Tovey

    is a professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. Dr. Tovey received an AB from Harvard College, an MS in computer science and a PhD in operations research from Stanford University. His principal activities are in operations research and its interdisciplinary applications. He received a Presidential Young Investigator Award and the Jacob Wolfowitz Prize for research in heuristics. He was named an Institute Fellow at Georgia Tech, and was recognized by the ACM Special Interest Group on Electronic Commerce with the Test of Time Award. Dr. Tovey received the 2016 Golden Goose Award for his research on bee foraging behavior leading to the development of the Honey Bee Algorithm.


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