Numerical Experience with Multiple Update Quasi-Newton Methods for Unconstrained Optimization

摘要

The authors have derived what they termed quasi-Newton multi step methods in [2]. These methods have demonstrated substantial numerical improvements over the standard single step Secant-based BFGS. Such methods use a variant of the Secant equation that the updated Hessian (or its inverse) satisfies at each iteration. In this paper, new methods will be explored for which the updated Hessians satisfy multiple relations of the Secant-type. A rational model is employed in developing the new methods. The model hosts a free parameter which is exploited in enforcing symmetry on the updated Hessian approximation matrix thus obtained. The numerical performance of such techniques is then investigated and compared to other methods. Our results are encouraging and the improvements incurred supercede those obtained from other existing methods at minimal extra storage and computational overhead.