Relaxation techniques for strictly convex network problems

S. A. Zenios, J. M. Mulvey

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Gauss-Seidel type relaxation techniques are applied in the context of strictly convex pure networks with separable cost functions. The algorithm is an extension of the Bertsekas-Tseng approach for solving the linear network problem and its dual as a pair of monotropic programming problems. The method is extended to cover the class of generalized network problems. Alternative internal tactics for the dual problem are examined. Computational experiments - aimed at the improved efficiency of the algorithm - are presented.

Original languageEnglish (US)
Pages (from-to)517-538
Number of pages22
JournalAnnals of Operations Research
Issue number2
StatePublished - Jun 1986

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • Management Science and Operations Research


  • Networks
  • dual coordinate descent
  • nonlinear programming
  • relaxation


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