Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems. They can also handle box constraints on parameters.
Ravi Varadhan, Johns Hopkins University
URL: http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.html
Hans W. Borchers, ABB Corporate Research
Maintainer: Ravi Varadhan <ravi.varadhan@jhu.edu>
| Package: | dfoptim |
| Type: | Package |
| Version: | 2023.1.0 |
| Date: | 2023-08-21 |
| License: | GPL-2 or greater |
| LazyLoad: | yes |
Derivative-Free optimization algorithms. These algorithms do not require gradient information.
More importantly, they can be used to solve non-smooth optimization problems.
These algorithms were translated from the Matlab code of Prof. C.T. Kelley, given in his book "Iterative methods for optimization".
However, there are some non-trivial modifications of the algorithm.
Currently, the Nelder-Mead and Hooke-Jeeves algorithms is implemented. In future, more derivative-free algorithms may be added.
C.T. Kelley (1999), Iterative Methods for Optimization, SIAM.