A flexible derivative-free solver for (bound constrained) nonlinear least-squares minimization

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  • License: GNU General Public License v3 or later (GPLv3+) (GPL-3.0-or-later)
  • Author: Lindon Roberts
  • Maintainer: Lindon Roberts
  • Tags mathematics , optimization , least squares , derivative free optimization , nonlinear least squares
  • Requires: Python >=3.9
  • Provides-Extra: dev , trustregion
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Project description

Build Status GNU GPL v3 License Latest PyPI version DOI:10.5281/zenodo.2630426 Total downloads

DFO-LS is a flexible package for solving nonlinear least-squares minimization, without requiring derivatives of the objective. It is particularly useful when evaluations of the objective function are expensive and/or noisy. DFO-LS is more flexible version of DFO-GN.

The main algorithm is described in our paper [1] below.

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.

Citation

The development of DFO-LS is outlined over several publications:

  1. C Cartis, J Fiala, B Marteau and L Roberts, Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers, ACM Transactions on Mathematical Software, 45:3 (2019), pp. 32:1-32:41 [preprint arXiv 1804.00154] .

  2. M Hough and L Roberts, Model-Based Derivative-Free Methods for Convex-Constrained Optimization, SIAM Journal on Optimization, 21:4 (2022), pp. 2552-2579 [preprint arXiv 2111.05443].

  3. Y Liu, K H Lam and L Roberts, Regularized black-box optimization algorithms for least-squares problems, IMA Journal of Numerical Analysis, 2025 [preprint arXiv 2407.14915].

If you use DFO-LS in a paper, please cite [1]. If your problem has constraints, including bound constraints, please cite [1,2]. If your problem includes a regularizer, please cite [1,3].

Requirements

DFO-LS 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):

Optional package: DFO-LS versions 1.2 and higher also support the trustregion package for fast trust-region subproblem solutions. To install this, make sure you have a Fortran compiler (e.g. gfortran) and NumPy installed, then run pip install trustregion. You do not have to have trustregion installed for DFO-LS to work, and it is not installed by default.

Installation using conda

DFO-LS can be directly installed in Anaconda environments using conda-forge:

$ conda install -c conda-forge dfo-ls

Installation using pip

For easy installation, use pip as root:

$ pip install DFO-LS

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

$ 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:

$ pip install .

To upgrade DFO-LS to the latest version, navigate to the top-level directory (i.e. the one containing pyproject.toml) and rerun the installation using pip, as above:

$ git pull
$ pip install .

Testing

If you installed DFO-LS manually, you can test your installation using the pytest package:

$ pip install pytest
$ python -m pytest --pyargs dfols

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:

$ 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. Please contact NAG for alternative licensing.

Project details

Verified details

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  • License: GNU General Public License v3 or later (GPLv3+) (GPL-3.0-or-later)
  • Author: Lindon Roberts
  • Maintainer: Lindon Roberts
  • Tags mathematics , optimization , least squares , derivative free optimization , nonlinear least squares
  • Requires: Python >=3.9
  • Provides-Extra: dev , trustregion
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