README.md
In hwborchers/dfoptim: Derivative-Free Optimization
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.
hwborchers/dfoptim documentation built on May 12, 2019, 4:26 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.