n 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 / DI PILLO, Gianni; Grippo, Luigi; Lucidi, Stefano. - STAMPA. - 84(1986), pp. 694-703. [10.1007/BFb0043895].
Globally Convergent Exact Penalty Algorithms for Constrained Optimization
DI PILLO, Gianni;GRIPPO, Luigi;LUCIDI, Stefano
1986
Abstract
n 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.