Relaxation techniques for strictly convex network problems

S. A. Zenios, J. M. Mulvey

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

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
Volume5
Issue number2
DOIs
StatePublished - Jun 1 1986

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Management Science and Operations Research

Keywords

  • Networks
  • dual coordinate descent
  • nonlinear programming
  • relaxation

Fingerprint Dive into the research topics of 'Relaxation techniques for strictly convex network problems'. Together they form a unique fingerprint.

  • Cite this