Constrained nonlinear least squares: an exact penalty approach with projected structured quasi-Newton updates

Abstract

This paper is concerned with the development, numerical implementation, and testing of an algorithm for solving constrained nonlinear least squares problems. The algorithm is an adaptation of the least squares case of an exact penalty method for solving nonlinearly constrained optimization problems due to Coleman and Conn. It also uses the ideas of Nocedal and Overton for handling quasi-Newton updates of projected Hessians, those of Dennis, Gay, and Welsch for approaching the structure of nonlinear least squares Hessians, and those of Murray and Overton for performing line searches. This method has been tested on a selection of problems listed in the collection of Hock and Schittkowski. The results indicate that the approach taken here is a viable alternative for least squares problems to the general nonlinear methods studied by Hock and Schittkowski.