Newton Algorithms for Large-Scale Strictly Convex Separable Network Optimization

  • Aleksandar Donev
  • Phillip Duxbury
    • Categories Convex Optimization, Network Optimization Tags , Short URL: https://optimization-online.org/?p=8550

    In this work we summarize the basic elements of primal and dual Newton algorithms for network optimization with continuously differentiable (strictly) convex arc cost functions. Both the basic mathematics and implementation are discussed, and hints to important tuning details are made. The exposition assumes that the reader posseses a significant level of prior knowledge in the field. The algorithms have been drawn from a very large pool of literature spanning over 20 years of research in the area. Please visit http://computation.pa.msu.edu/NO

    Citation

    Working internal report, Computational Materials Science Group (Prof. Phil Duxbuty), Physics and Astronomy Department, Michigan State University, January 2001

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