Computational Optimization and Applications

, Volume 13, Issue 1, pp 231–252

An Interior-Point Algorithm for Nonconvex Nonlinear Programming

Authors

  • Robert J. Vanderbei
    • Princeton University
  • David F. Shanno
    • Princeton University
Article

DOI: 10.1023/A:1008677427361

Cite this article as:
Vanderbei, R.J. & Shanno, D.F. Computational Optimization and Applications (1999) 13: 231. doi:10.1023/A:1008677427361

Abstract

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 merit function and an altered search direction to ensure that a descent direction for the merit function is obtained. 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.

nonlinear programminginterior-point methodsnonconvex optimization

Copyright information

© Kluwer Academic Publishers 1999