A Computational Comparison of Gradient Minimization Algorithms.
AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OHIO SCHOOL OF ENGINEERING
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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 Davidons 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. Davidons 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
- Theoretical Mathematics
- Operations Research