Abstract In this paper we define two classes of algorithms for the solution of constrained problems. The first class is based on a continuously differentiable exact penalty function, with the additional inclusion of a barrier term. The second class is based on a similar modification performed on a continuously differentiable exact augmented Lagrangian function. In connection with these functions, an automatic adjustment rule for the penalty parameter is described, which ensures global convergence, and Newton-type schemes are proposed which ensure an ultimate superlinear convergence rate.
Globally convergent exact penalty algorithms for constrained optimization
1986-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Globally Convergent Autocalibration
British Library Conference Proceedings | 2003
|Globally convergent autocalibration
IEEE | 2003
|