Steplength selection in interior-point methods for quadratic programming

Frank Curtis, Jorge Nocedal*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


We present a new strategy for choosing primal and dual steplengths in a primal-dual interior-point algorithm for convex quadratic programming. Current implementations often scale steps equally to avoid increases in dual infeasibility between iterations. We propose that this method can be too conservative, while safeguarding an unequally-scaled steplength approach will often require fewer steps toward a solution. Computational results are given.

Original languageEnglish (US)
Pages (from-to)516-523
Number of pages8
JournalApplied Mathematics Letters
Issue number5
StatePublished - May 2007


  • Barrier method
  • Interior-point method
  • Nonlinear optimization
  • Quadratic programming

ASJC Scopus subject areas

  • Applied Mathematics


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