Skip to main content
Log in

A nonlinear programming algorithm based on non-coercive penalty functions

  • Published:
Mathematical Programming Submit manuscript


 We consider first the differentiable nonlinear programming problem and study the asymptotic behavior of methods based on a family of penalty functions that approximate asymptotically the usual exact penalty function. We associate two parameters to these functions: one is used to control the slope and the other controls the deviation from the exact penalty.

We propose a method that does not change the slope for feasible iterates and show that for problems satisfying the Mangasarian-Fromovitz constraint qualification all iterates will remain feasible after a finite number of iterations. The same results are obtained for non-smooth convex problems under a Slater qualification condition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Author information

Authors and Affiliations


Additional information

Received: September 2000 / Accepted: June 2002 Published online: March 21, 2003

Research partially supported by CAPES, Brazil

Research partially supported by CNPq, Brazil, and CONICIT, Venezuela.

Mathematics Subject Classification (1991): 20E28, 20G40, 20C20

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gonzaga, C., Castillo, R. A nonlinear programming algorithm based on non-coercive penalty functions. Math. Program., Ser. A 96, 87–101 (2003).

Download citation

  • Issue Date:

  • DOI: