A flexible derivative-free solver for (bound constrained) nonlinear least-squares minimization
Project description
DFO-LS is a flexible package for solving nonlinear least-squares minimisation, without requiring derivatives of the objective.
This is an implementation of the algorithm from our paper: C. Cartis, J. Fiala, B. Marteau and L. Roberts, Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers, technical report, University of Oxford, (2018).
Documentation
See manual.pdf or here.
Requirements
DFO-LS requires the following software to be installed:
Python 2.7 or Python 3 (http://www.python.org/)
Additionally, the following python packages should be installed (these will be installed automatically if using pip, see Installation using pip):
NumPy 1.11 or higher (http://www.numpy.org/)
SciPy 0.18 or higher (http://www.scipy.org/)
Pandas 0.17 or higher (http://pandas.pydata.org/)
Installation using pip
For easy installation, use pip (http://www.pip-installer.org/) as root:
$ [sudo] pip install dfols
or alternatively easy_install:
$ [sudo] easy_install dfols
If you do not have root privileges or you want to install DFO-LS for your private use, you can use:
$ pip install --user dfols
which will install DFO-LS in your home directory.
Note that if an older install of DFO-LS is present on your system you can use:
$ [sudo] pip install --upgrade dfols
to upgrade DFO-LS to the latest version.
Manual installation
Alternatively, you can download the source code from Github and unpack as follows:
$ git clone https://github.com/numericalalgorithmsgroup/dfols $ cd dfols
DFO-LS is written in pure Python and requires no compilation. It can be installed using:
$ [sudo] pip install .
If you do not have root privileges or you want to install DFO-LS for your private use, you can use:
$ pip install --user .
instead.
Testing
If you installed DFO-LS manually, you can test your installation by running:
$ python setup.py test
Alternatively, the HTML documentation provides some simple examples of how to run DFO-LS.
Examples
Examples of how to run DFO-LS are given in the documentation, and the examples directory in Github.
Uninstallation
If DFO-LS was installed using pip you can uninstall as follows:
$ [sudo] pip uninstall dfols
If DFO-LS was installed manually you have to remove the installed files by hand (located in your python site-packages directory).
Bugs
Please report any bugs using GitHub’s issue tracker.
License
This algorithm is released under the GNU GPL license.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.