An Interior-Point Algorithm for Nonconvex Nonlinear Programming

Robert J. Vanderbei, David F. Shanno

Research output: Contribution to journalArticlepeer-review

368 Scopus citations


The paper describes an interior-point algorithm for nonconvex nonlinear programming which is a direct extension of interior-point methods for linear and quadratic programming. Major modifications include a Preliminary numerical testing indicates that the method is robust. Further, numerical comparisons with MINOS and LANCELOT show that the method is efficient, and has the promise of greatly reducing solution times on at least some classes of models.

Original languageEnglish (US)
Pages (from-to)231-252
Number of pages22
JournalComputational Optimization and Applications
Issue number1-3
StatePublished - 1999

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Control and Optimization
  • Applied Mathematics


  • Interior-point methods
  • Nonconvex optimization
  • Nonlinear programming


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