@Article{JCM-22-4,
author = {},
title = {A Direct Search Frame-Based Conjugate Gradients Method},
journal = {Journal of Computational Mathematics},
year = {2004},
volume = {22},
number = {4},
pages = {489--500},
abstract = {<p style="text-align: justify;">A derivative-free frame-based conjugate gradients algorithm is presented. Convergence is shown for $C^1$ functions, and this is verified in numerical trials. The algorithm is tested on a variety of low dimensional problems, some of which are ill-conditioned, and is also tested on problems of high dimension. Numerical results show that the algorithm is effective on both classes of problems. The results are compared with those from a discrete quasi-Newton method, showing that the conjugate gradients algorithm is competitive. The algorithm exhibits the conjugate gradients speed-up on problems for which the Hessian at the solution has repeated or clustered eigenvalues. The algorithm is easily parallelizable.</p>},
issn = {1991-7139},
doi = {https://doi.org/2004-JCM-8858},
url = {https://global-sci.com/article/85236/a-direct-search-frame-based-conjugate-gradients-method}
}