Mathematical Programming

, Volume 87, Issue 2, pp 215–249

A primal-dual trust-region algorithm for non-convex nonlinear programming

  • Andrew R. Conn
  • Nicholas I. M. Gould
  • Dominique Orban
  • Philippe L. Toint

DOI: 10.1007/s101070050112

Cite this article as:
Conn, A., Gould, N., Orban, D. et al. Math. Program. (2000) 87: 215. doi:10.1007/s101070050112

Abstract.

A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trust-region model to ensure descent on a suitable merit function. Convergence is proved to second-order critical points from arbitrary starting points. Numerical results are presented for general quadratic programs.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Andrew R. Conn
    • 1
  • Nicholas I. M. Gould
    • 2
  • Dominique Orban
    • 3
  • Philippe L. Toint
    • 4
  1. 1.IBM T.J. Watson Research Center, P.O.Box 218, Yorktown Heights, NY, USA, e-mail: arconn@watson.ibm.comUS
  2. 2.Rutherford Appleton Laboratory, Computational Science and Engineering Departement, Chilton, Oxfordshire, England, e-mail: n.gould@rl.ac.ukGB
  3. 3.CERFACS, 42 Avenue Gaspard Coriolis, 31057 Toulouse Cedex 1, France, e-mail: Dominique.Orban@cerfacs.frFR
  4. 4.Facultés Universitaires Notre-Dame de la Paix, 61, rue de Bruxelles, B-5000 Namur, Belgium, e-mail: Philippe.Toint@fundp.ac.beBE

Personalised recommendations