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

Navigation

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
Maintainers
Avatar for lindonroberts from gravatar.com lindonroberts

Unverified details

These details have not been verified by PyPI
Meta
  • 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
Classifiers
Report project as malware

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, Black-box Optimization Algorithms for Regularized Least-squares Problems, arXiv preprint arXiv:arXiv:2407.14915, 2024.

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

These details have been verified by PyPI
Project links
GitHub Statistics
Maintainers
Avatar for lindonroberts from gravatar.com lindonroberts

Unverified details

These details have not been verified by PyPI
Meta
  • 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
Classifiers

Release history Release notifications | RSS feed

This version

1.5.4

1.5.3

1.5.2

1.5.1

1.5.0

1.4.1

1.4.0

1.3.0

1.2.3

1.2.2

1.2.1

1.2

1.1.1

1.1

1.0.2

1.0.1

1.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dfo_ls-1.5.4.tar.gz (61.5 kB view details)

Uploaded Source

Built Distribution

DFO_LS-1.5.4-py3-none-any.whl (65.4 kB view details)

Uploaded Python 3

File details

Details for the file dfo_ls-1.5.4.tar.gz.

File metadata

  • Download URL: dfo_ls-1.5.4.tar.gz
  • Upload date:
  • Size: 61.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for dfo_ls-1.5.4.tar.gz
Algorithm Hash digest
SHA256 edf61b4c37a06130c54e5b78b49b106c349cf2549e5ae1e940f242c98e04b0f7
MD5 b62d509fe8405088589defeb202328d6
BLAKE2b-256 220437dec4281b206ef4001ca814074c85102b63a32f9faaabba6bb67229e0a4

See more details on using hashes here.

Provenance

The following attestation bundles were made for dfo_ls-1.5.4.tar.gz:

Publisher: upload_pypi.yml on numericalalgorithmsgroup/dfols

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file DFO_LS-1.5.4-py3-none-any.whl.

File metadata

  • Download URL: DFO_LS-1.5.4-py3-none-any.whl
  • Upload date:
  • Size: 65.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for DFO_LS-1.5.4-py3-none-any.whl
Algorithm Hash digest
SHA256 bc87e0679175a8ef0583771b94459ca3843593c0bfa6c528c1ade19117348896
MD5 55b3ffeb5a4987876b5e74e539752300
BLAKE2b-256 683500710059f975bfef068c1a6c39547efa1ef8dc6fb1b010e3bc85fb808c41

See more details on using hashes here.

Provenance

The following attestation bundles were made for DFO_LS-1.5.4-py3-none-any.whl:

Publisher: upload_pypi.yml on numericalalgorithmsgroup/dfols

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page