Skip to main content

Constrained Optimization BY Quadratic Approximations

Project description

https://img.shields.io/github/actions/workflow/status/cobyqa/cobyqa/build.yml?logo=github&style=for-the-badge https://img.shields.io/readthedocs/cobyqa/latest?logo=readthedocs&style=for-the-badge https://img.shields.io/codecov/c/github/cobyqa/cobyqa?logo=codecov&style=for-the-badge https://img.shields.io/pypi/v/cobyqa?logo=pypi&style=for-the-badge https://img.shields.io/pypi/dm/cobyqa?logo=pypi&style=for-the-badge https://img.shields.io/conda/v/conda-forge/cobyqa?logo=anaconda&style=for-the-badge&label=conda-forge https://img.shields.io/conda/d/conda-forge/cobyqa?logo=anaconda&style=for-the-badge&label=downloads

COBYQA, an acronym for Constrained Optimization BY Quadratic Approximations, is designed to supersede COBYLA as a general derivative-free optimization solver. It can handle unconstrained, bound-constrained, linearly constrained, and nonlinearly constrained problems. It uses only function values of the objective and constraint functions, if any. No derivative information is needed.

Documentation: https://www.cobyqa.com.

Installation

COBYQA can be installed for Python 3.8 or above.

Dependencies

The following Python packages are required by COBYQA:

  • NumPy 1.17.0 or higher, and

  • SciPy 1.10.0 or higher.

If you install COBYQA using pip or conda (see below), these dependencies will be installed automatically. More dependencies are required to run the test suite and some examples given in the folder examples. See the relevant sections below for more details.

User installation

The easiest way to install COBYQA is using pip or conda. To install it using pip, run in a terminal or command window

pip install cobyqa

If you are using conda, you can install COBYQA from the conda-forge channel by running

conda install conda-forge::cobyqa

To check your installation, you can execute

python -c "import cobyqa; cobyqa.show_versions()"

If your python launcher is not python, you can replace it with the appropriate command (similarly for pip and conda). For example, you may need to use python3 instead of python and pip3 instead of pip.

Testing

To execute the test suite of COBYQA, you first need to install pytest. You can then run the test suite by executing

pytest --pyargs cobyqa

The test suite takes several minutes to run. It is unnecessary to run the test suite if you installed COBYQA using the recommended method described above.

Examples

The folder examples contains a few examples of how to use COBYQA. To run powell2015.py, you first need to install matplotlib. These files contains headers explaining what problems they solve.

Support

To report a bug or request a new feature, please open a new issue using the issue tracker.

Project details


Download files

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

Source Distribution

cobyqa-1.1.1.tar.gz (57.0 kB view details)

Uploaded Source

Built Distribution

cobyqa-1.1.1-py3-none-any.whl (56.9 kB view details)

Uploaded Python 3

File details

Details for the file cobyqa-1.1.1.tar.gz.

File metadata

  • Download URL: cobyqa-1.1.1.tar.gz
  • Upload date:
  • Size: 57.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.2

File hashes

Hashes for cobyqa-1.1.1.tar.gz
Algorithm Hash digest
SHA256 5c108b9ac7e0184d6d7003d6b4760550c74659597080d042eaba49e8f483c18c
MD5 89e68d8fe6498ef3ac36ce4ad72b4770
BLAKE2b-256 0652084b2740e045c7aa9b29374388de9f572f95b0b3544f4e37ad1c9996d026

See more details on using hashes here.

Provenance

File details

Details for the file cobyqa-1.1.1-py3-none-any.whl.

File metadata

  • Download URL: cobyqa-1.1.1-py3-none-any.whl
  • Upload date:
  • Size: 56.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.2

File hashes

Hashes for cobyqa-1.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 9a90e69db4b2ad56c30f88d862c99e1efb47e2ba10ee743b7fa84157ac364855
MD5 e7a436e2c594617ad40ba8e2e278e975
BLAKE2b-256 22aa2ebf198eeffc08820c8676d6004f775c46e9bbe3c70199f905e8e088e28a

See more details on using hashes here.

Provenance

Supported by

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