Vol 3 No 1 (2008)
Articles

Global convergence of a primal-dual interior-point method for nonlinear programming

Igor Griva
George Mason University, Departments of Math Sciences and CDS, Fairfax, VA 22030
David F. Shanno
Rutgers University, RUTCOR, New Brunswick, NJ 08903
Robert J. Vanderbei
Princeton University, Department of ORFE, Princeton NJ 08544
Hande Y. Benson
Drexel University, Department of Decision Sciences, Philadelphia, PA 19104
Keywords
  • Interior-point method,
  • primal-dual,
  • convergence analysis
How to Cite
Griva, I., Shanno, D. F., Vanderbei, R. J., & Benson, H. Y. (1). Global convergence of a primal-dual interior-point method for nonlinear programming. Algorithmic Operations Research, 3(1). Retrieved from https://journals.lib.unb.ca/index.php/AOR/article/view/5665

Abstract

Many recent convergence results obtained for primal-dual interior-point methods for nonlinear programming, use assumptions of the boundedness of generated iterates. In this paper we replace such assumptions by new assumptions on the NLP problem, develop a modification of a primal-dual interior-point method implemented in software package LOQO and analyze convergence of the new method from any initial guess.