Linear Optimization and Duality

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A01=Craig A. Tovey
Author_Craig A. Tovey
Barrier Algorithms
Basic Feasible Solution
Basic Solution
Binary Heap
Category=PBWH
Category=UYA
Complementary Slackness
Dual Feasibility
Dual Simplex Method
Dual Variable
Elementary Row Operations
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eq_computing
eq_isMigrated=1
eq_nobargain
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integer programming
Linearly Independent
LP Duality
LP Model
LP Relaxation
Nonbasic Variable
Nonnegative Linear Combinations
Optimal Dual Solution
Pivot Row
polyhedral theory
Polynomial Time
Polynomial Time Algorithm
primal dual methods
rigorous mathematics for optimisation students
sensitivity analysis
Shadow Price
Simplex Algorithm
Simplex Method
Slack Variables
Strong Duality Theorem
Upper Bounds
zone of proximal development

Product details

  • ISBN 9781439887462
  • Weight: 1440g
  • Dimensions: 178 x 254mm
  • Publication Date: 12 Nov 2020
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Linear Optimization and Duality: 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.

Craig A. Tovey is a professor at Georgia Tech. Institute.

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