A Filter Active-Set Trust-Region Method

Michael P. Friedlander(mpfcs.ubc.ca)
Nick I. M. Gould(gouldcombal.ox.ac.uk)
Sven Leyffer(leyffermcs.anl.gov)
Todd S. Munson(tmunsonmcs.anl.gov)

Abstract: We develop a new active-set method for nonlinear programming problems that solves a regularized linear program to predict the active set and then fixes the active constraints to solve an equality-constrained quadratic program for fast convergence. Global convergence is promoted through the use of a filter. We show that the regularization parameter fulfills the same role as a trust-region parameter, and we give global convergence results. In addition, we show that the method identifies the optimal active set once it is sufficiently close to a regular solution. We also comment on alternative regularized problems that allow the inclusion of curvature information into the active-set identification.

Keywords: Nonlinear programming, active-set methods, filter methods

Category 1: Nonlinear Optimization

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: Preprint ANL/MCS-P1456-0907, Argonne, IL, September 10, 2007

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Entry Submitted: 09/11/2007
Entry Accepted: 09/11/2007
Entry Last Modified: 09/11/2007

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