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Solver\n======================================================\n\n.. image::  https://travis-ci.org/numericalalgorithmsgroup/dfogn.svg?branch=master\n   :target: https://travis-ci.org/numericalalgorithmsgroup/dfogn\n   :alt: Build Status\n\n.. image::  https://img.shields.io/badge/License-GPL%20v3-blue.svg\n   :target: https://www.gnu.org/licenses/gpl-3.0\n   :alt: GNU GPL v3 License\n\n.. image:: https://img.shields.io/pypi/v/DFOGN.svg\n   :target: https://pypi.python.org/pypi/DFOGN\n   :alt: Latest PyPI version\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2629875.svg\n   :target: https://doi.org/10.5281/zenodo.2629875\n   :alt: DOI:10.5281/zenodo.2629875\n\nDFO-GN is a package for solving nonlinear least-squares minimisation, without requiring derivatives of the objective.\n\nThis is an implementation of the algorithm from our paper:\n`A Derivative-Free Gauss-Newton Method \u003chttps://doi.org/10.1007/s12532-019-00161-7\u003e`_, C. Cartis and L. Roberts, Mathematical Programming Computation (2019). For reproducibility of all figures in this paper, please feel free to contact the authors. A preprint of the paper is available `here \u003chttps://arxiv.org/abs/1710.11005\u003e`_.\n\nNote: we have released a newer package, called DFO-LS, which is an upgrade of DFO-GN to improve its flexibility and robustness to noisy problems. See `here \u003chttps://github.com/numericalalgorithmsgroup/dfols\u003e`_ for details.\n\n**Citation** To cite DFO-GN, please use\n::\n   @Article{DFOGN,\n     Title    = {A derivative-free {G}auss-{N}ewton method},\n     Author   = {Cartis, Coralia and Roberts, Lindon},\n     Journal  = {Mathematical Programming Computation},\n     Year     = {2019},\n     Doi      = {10.1007/s12532-019-00161-7},\n     Url      = {https://doi.org/10.1007/s12532-019-00161-7}\n   }\n\nDocumentation\n-------------\nSee manual.pdf or `here \u003chttps://numericalalgorithmsgroup.github.io/dfogn/\u003e`_.\n\nRequirements\n------------\nDFO-GN requires the following software to be installed:\n\n* `Python 2.7 or Python 3 \u003chttp://www.python.org/\u003e`_\n\nAdditionally, the following python packages should be installed (these will be installed automatically if using `pip \u003chttp://www.pip-installer.org/\u003e`_, see `Installation using pip`_):\n\n* `NumPy 1.11 or higher \u003chttp://www.numpy.org/\u003e`_ \n* `SciPy 0.18 or higher \u003chttp://www.scipy.org/\u003e`_\n\n\nInstallation using pip\n----------------------\nFor easy installation, use `pip \u003chttp://www.pip-installer.org/\u003e`_ as root:\n\n .. code-block:: bash\n\n    $ [sudo] pip install --pre dfogn\n\nIf you do not have root privileges or you want to install DFO-GN for your private use, you can use:\n\n .. code-block:: bash\n\n    $ pip install --pre --user dfogn\n      \nwhich will install DFO-GN in your home directory.\n\nNote that if an older install of DFO-GN is present on your system you can use:\n\n .. code-block:: bash\n\n    $ [sudo] pip install --pre --upgrade dfogn\n      \nto upgrade DFO-GN to the latest version.\n\nManual installation\n-------------------\nThe source code for DFO-GN is `available on Github \u003chttps://https://github.com/numericalalgorithmsgroup/dfogn\u003e`_:\n\n .. code-block:: bash\n \n    $ git clone https://github.com/numericalalgorithmsgroup/dfogn\n    $ cd dfogn\n\nDFO-GN is written in pure Python and requires no compilation. It can be installed using:\n\n .. code-block:: bash\n\n    $ [sudo] pip install --pre .\n\nIf you do not have root privileges or you want to install DFO-GN for your private use, you can use:\n\n .. code-block:: bash\n\n    $ pip install --pre --user .\n    \ninstead.    \n\nTesting\n-------\nIf you installed DFO-GN manually, you can test your installation by running:\n\n .. code-block:: bash\n\n    $ python setup.py test\n\nAlternatively, the `documentation \u003chttps://numericalalgorithmsgroup.github.io/dfogn/\u003e`_ provides some simple examples of how to run DFO-GN, which are also available in the examples directory.\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnumericalalgorithmsgroup%2Fdfogn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnumericalalgorithmsgroup%2Fdfogn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnumericalalgorithmsgroup%2Fdfogn/lists"}