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

Authors

  • 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

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.

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How to Cite

Griva, I., Shanno, D. F., Vanderbei, R. J., & Benson, H. Y. (2008). 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

Issue

Vol. 3 No. 1 (2008)

Section

Articles

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