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A derivative-free solver for large-scale nonlinear least-squares minimization

Project description

Build Status GNU GPL v3 License

DFBGN is a Python package for nonlinear least-squares minimization, where derivatives are not available. It is particularly useful when evaluations of the objective are expensive and/or noisy, and the number of variables to be optimized is large.

DFBGN is based on DFO-LS, but is better-suited when there are many variables to be optimized (so the linear algebra in DFO-LS is too slow). Unlike DFO-LS, DFBGN does not currently support bound constraints on the variables.

If you are interested in solving general optimization problems (without a least-squares structure), you may wish to try Py-BOBYQA.

Requirements

DFBGN requires the following software to be installed:

Additionally, the following python packages should be installed (these will be installed automatically if using pip, see Installation using pip):

Installation using pip

For easy installation, use pip as root:

$ [sudo] pip install dfbgn

or alternatively easy_install:

$ [sudo] easy_install dfbgn

If you do not have root privileges or you want to install DFBGN for your private use, you can use:

$ pip install --user dfbgn

which will install DFBGN in your home directory.

Note that if an older install of DFBGN is present on your system you can use:

$ [sudo] pip install --upgrade dfbgn

to upgrade DFBGN 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/dfbgn
$ cd dfbgn

DFBGN 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 DFBGN for your private use, you can use:

$ pip install --user .

instead.

To upgrade DFBGN to the latest version, navigate to the top-level directory (i.e. the one containing setup.py) and rerun the installation using pip, as above:

$ git pull
$ [sudo] pip install .  # with admin privileges

Testing

If you installed DFBGN manually, you can test your installation by running:

$ python setup.py test

Alternatively, the HTML documentation provides some simple examples of how to run DFBGN.

Examples

Examples of how to run DFBGN are given in the documentation, and the examples directory in Github.

Uninstallation

If DFBGN was installed using pip you can uninstall as follows:

$ [sudo] pip uninstall dfbgn

If DFBGN 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. Please contact NAG for alternative licensing.

Project details


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0.1

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