On limited memory SQP method for large scale equality constrained nonlinear least squares
by
Zhengfeng Li
CMA,ANU

This paper describes the implementation of limited memory SQP methods (LSQP) for large scale equality constrainted nonlinear least squares. By introducing additional variables, the original problem is transformed into a general equality constrained nonlinear programming problem with a simple objective. This is then solved by a limited memory variation of the SQP method. This proposed method overcomes one of the major drawbacks of the trantional SQP method where a large matrix needs to be calculated and stored. For a better performance, the special structure of the problem has beed fully exploited. We compare the performance of the new method with that of the reduced hessian SQP method (RSQP) developed by Biegler etc. (1997). Our numerical tests indicate that the new method is faster than the RSQP method, and is better able to use additional storage to accelerate convergence. For some problems it approaches the performance of the full Hessian SQP method adapted for least squares problems by Schittkowski (1988). However, his method can not cope with problems with very many observations. We also find that the LSQP method can be greatly accelerated by means of a simple restart procedure

Date received: July 25, 1999

Copyright © 1999 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # cadk-48.