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  3. Iterative Methods in Combinatorial Optimization
  • Cited by 87
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    • Publisher:
      Cambridge University Press
      Publication date:
      June 2012
      April 2011
      ISBN:
      9780511977152
      9781107007512
      9780521189439
      DOI:
      https://doi.org/10.1017/CBO9780511977152
      Dimensions:
      (228 x 152 mm)
      Weight & Pages:
      0.48kg, 254 Pages
      Dimensions:
      (228 x 152 mm)
      Weight & Pages:
      0.36kg, 256 Pages
      • Subjects:
      Algorithmics, Complexity, Computer Algebra, Computational Geometry, Differential and Integral Equations, Dynamical Systems and Control Theory, Mathematical Physics, Computer Science, Optimization, OR and risk, Statistics and Probability
      Series:
      Cambridge Texts in Applied Mathematics (46)
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    • Selected: Digital
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    Subjects:
    Algorithmics, Complexity, Computer Algebra, Computational Geometry, Differential and Integral Equations, Dynamical Systems and Control Theory, Mathematical Physics, Computer Science, Optimization, OR and risk, Statistics and Probability
    Series:
    Cambridge Texts in Applied Mathematics (46)
    Information

    Book description

    With the advent of approximation algorithms for NP-hard combinatorial optimization problems, several techniques from exact optimization such as the primal-dual method have proven their staying power and versatility. This book describes a simple and powerful method that is iterative in essence and similarly useful in a variety of settings for exact and approximate optimization. The authors highlight the commonality and uses of this method to prove a variety of classical polyhedral results on matchings, trees, matroids and flows. The presentation style is elementary enough to be accessible to anyone with exposure to basic linear algebra and graph theory, making the book suitable for introductory courses in combinatorial optimization at the upper undergraduate and beginning graduate levels. Discussions of advanced applications illustrate their potential for future application in research in approximation algorithms.

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