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Identifying the Optimal Face of a Network Linear Program with a Globally Convergent Interior Point Method

In: Large Scale Optimization

Author

Listed:
  • Mauricio G. C. Resende

    (AT&T Bell Laboratories)

  • Takashi Tsuchiya

    (The Institute of Statistical Mathematics)

  • Geraldo Veiga

    (University of California)

Abstract

Based on recent convergence results for the affine scaling algorithm for linear programming, we investigate strategies to identify the optimal face of a minimum cost network flow problem. In the computational experiments described, one of the proposed optimality indicators is used to implement an early stopping criterion in DLNET, an implementation of the dual affine scaling algorithm for solving minimum cost network flow problems. We conclude from the experiments that the new indicator is far more robust than the one used in earlier versions of DLNET.

Suggested Citation

  • Mauricio G. C. Resende & Takashi Tsuchiya & Geraldo Veiga, 1994. "Identifying the Optimal Face of a Network Linear Program with a Globally Convergent Interior Point Method," Springer Books, in: W. W. Hager & D. W. Hearn & P. M. Pardalos (ed.), Large Scale Optimization, pages 362-387, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4613-3632-7_18
    DOI: 10.1007/978-1-4613-3632-7_18
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