A Computational Comparison of Gradient Minimization Algorithms.
Abstract
A technique was developed for the comparison of gradient minimization routines in solving the unconstrained optimization problem. The problem of locating the local minimum of a given real-valued, non-negative, differentiable function was used in this study to compare three gradient algorithms, namely Davidon's variance algorithm, the Fletcher-Reeves algorithm, and the Fletcher-Powell algorithm. A cost criterion and an average convergence rate were devised to facilitate the comparisons of these algorithms. Davidon's variance algorithm, a rank-one method, was judged to be the best routine for over fifty-three percent of the total cases tested. The comparisons are graphically presented in an appendix. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1971
- Accession Number
- AD0730159
Entities
People
- Craig E. Miller
Organizations
- Air Force Institute of Technology