Nonlinear Programming on Generalized Networks

David P. Ahlfeld, John M. Mulvey, Ron S. Dembo, Stavros A. Zenios

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


We describe a specialization of the primal truncated Newton algorithm for solving nonlinear optimization problems on networks with gains. The algorithm and its implementation are able to capitalize on the special structure of the constraints. Extensive computational tests show that the algorithm is capable of solving very large problems. Testing of numerous tactical issues are described, including maximal basis, projected line search, and pivot strategies. Comparisons with NLPNET, a nonlinear network code, and MINOS, a general-purpose nonlinear programming code, are also included.

Original languageEnglish (US)
Pages (from-to)350-367
Number of pages18
JournalACM Transactions on Mathematical Software (TOMS)
Issue number4
StatePublished - Dec 1 1987

All Science Journal Classification (ASJC) codes

  • Software
  • Applied Mathematics


  • Generalized networks
  • Newton's method
  • network optimization
  • nonlinear programming


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