A distributed algorithm for convex network optimization problems

Stavros A. Zenios, John M. Mulvey

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Gauss-Seidel type relaxation techniques are applied in the context of strictly convex network optimization problems. The algorithm lends itself for processing in a massively distributed environment. A synchronous relaxation method (SRM) is proposed, based on the k-coloring properties of the network graph. The method is tested in a simulated distributed environment on a sequential machine. Lower bounds for the expected efficiency of SRM are developed and compared with its performance as obtained through computional experiments.

Original languageEnglish (US)
Pages (from-to)45-56
Number of pages12
JournalParallel Computing
Issue number1
StatePublished - Jan 1988

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence


  • Networks
  • distributed systems
  • nonlinear programming
  • optimization


Dive into the research topics of 'A distributed algorithm for convex network optimization problems'. Together they form a unique fingerprint.

Cite this