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. It is particularly useful when evaluations of the objective function are expensive and/or noisy.
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). DFO-LS is more flexible version of DFO-GN.
If you are interested in solving general optimization problems (without a least-squares structure), you may wish to try Py-BOBYQA, which has many of the same features as DFO-LS.
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 as root:
$ [sudo] pip install DFO-LS
or alternatively easy_install:
$ [sudo] easy_install DFO-LS
If you do not have root privileges or you want to install DFO-LS for your private use, you can use:
$ pip install --user DFO-LS
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 DFO-LS
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 DFO-LS
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.
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