dfoptim-package: Derivative-Free Optimization

In dfoptim: Derivative-Free Optimization

Derivative-Free Optimization

Description

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.

Details

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.

Author(s)

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 <[e-mail]>

References

C.T. Kelley (1999), Iterative Methods for Optimization, SIAM.

dfoptim documentation built on Aug. 23, 2023, 5:10 p.m.