# IBCDFO
Interpolation-Based Composite Derivative-Free Optimization
[](https://github.com/psf/black)
[](https://coveralls.io/github/POptUS/IBCDFO?branch=main)
This page contains source code for interpolation-based optimization methods for
composite derivative-free optimization.
Relevant references include:
- J. Larson and M. Menickelly. Structure-aware methods for expensive
derivative-free nonsmooth composite optimization. *arXiv:2207.08264*. 2022.
[LINK](https://arxiv.org/abs/2207.08264)
- J. Larson, M. Menickelly, and B. Zhou. Manifold sampling for optimizing
nonsmooth nonconvex compositions. *SIAM Journal on Optimization*.
31(4):2638–2664, 2021
[DOI](https://doi.org/10.1137/20M1378089)
- K. A. Khan, J. Larson, and S. M. Wild. Manifold sampling for optimization of
nonconvex functions that are piecewise linear compositions of smooth
components. *SIAM Journal on Optimization* 28(4):3001--3024, 2018,
[DOI](https://doi.org/10.1137/17m114741x)
- S. M. Wild. POUNDERS in TAO: Solving Derivative-Free Nonlinear
Least-Squares Problems with POUNDERS. *Advances and Trends in Optimization with
Engineering Applications*. SIAM. 529--539, 2017.
[DOI](https://doi.org/10.1137%2F1.9781611974683.ch40)
- J. Larson, M. Menickelly, and S. M. Wild. Manifold sampling for l1 nonconvex
optimization. *SIAM Journal on Optimization*. 26(4):2540–2563, 2016.
[DOI](https://doi.org/10.1137/15M1042097)
## Contributing to IBCDFO
Contributions are welcome in a variety of forms; please see [CONTRIBUTING](CONTRIBUTING.rst).
## Installation & Updating
Note that this repository depends on one or more submodules. After cloning
this repository, from within the clone please run
``git submodule update --init --recursive``
to fetch all files contained in the submodules. This must be done before
attempting to use the code in the clone. Issuing the command `git pull` will
update the repository, but not the submodules. To update the clone and all its
submodules simultaneously, run
``git pull --recurse-submodules``.
The `ibcdfo` python package can be installed by setting up a terminal with the
target python and pip pair and executing
```
> pushd ibcdfo_pypkg
> python setup.py sdist
> pip install dist/ibcdfo-<version>.tar.gz
> popd
```
where `<version>` can be determined by looking at the output of the `sdist`
command. The installation can be partially tested by executing
```
> python
>>> import ibcdfo
>>> ibcdfo.__version__
<version>
```
where the output `<version>` should be identical to the value used during
installation.
## License
All code included in IBCDFO is open source, with the particular form of license contained in the top-level
subdirectories. If such a subdirectory does not contain a LICENSE file, then it is automatically licensed
as described in the otherwise encompassing IBCDFO [LICENSE](/LICENSE).
## Resources
To seek support or report issues, e-mail:
* ``poptus@mcs.anl.gov``
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"description": "# IBCDFO\n\nInterpolation-Based Composite Derivative-Free Optimization\n\n\n[](https://github.com/psf/black)\n[](https://coveralls.io/github/POptUS/IBCDFO?branch=main)\n\nThis page contains source code for interpolation-based optimization methods for\ncomposite derivative-free optimization.\n\nRelevant references include:\n\n - J. Larson and M. Menickelly. Structure-aware methods for expensive\n derivative-free nonsmooth composite optimization. *arXiv:2207.08264*. 2022.\n [LINK](https://arxiv.org/abs/2207.08264)\n\n - J. Larson, M. Menickelly, and B. Zhou. Manifold sampling for optimizing\n nonsmooth nonconvex compositions. *SIAM Journal on Optimization*.\n 31(4):2638\u20132664, 2021\n [DOI](https://doi.org/10.1137/20M1378089)\n\n - K. A. Khan, J. Larson, and S. M. Wild. Manifold sampling for optimization of\n nonconvex functions that are piecewise linear compositions of smooth\n components. *SIAM Journal on Optimization* 28(4):3001--3024, 2018,\n [DOI](https://doi.org/10.1137/17m114741x)\n\n - S. M. Wild. POUNDERS in TAO: Solving Derivative-Free Nonlinear\n Least-Squares Problems with POUNDERS. *Advances and Trends in Optimization with\n Engineering Applications*. SIAM. 529--539, 2017.\n [DOI](https://doi.org/10.1137%2F1.9781611974683.ch40)\n\n - J. Larson, M. Menickelly, and S. M. Wild. Manifold sampling for l1 nonconvex\n optimization. *SIAM Journal on Optimization*. 26(4):2540\u20132563, 2016.\n [DOI](https://doi.org/10.1137/15M1042097)\n\n## Contributing to IBCDFO\n\nContributions are welcome in a variety of forms; please see [CONTRIBUTING](CONTRIBUTING.rst).\n\n## Installation & Updating\nNote that this repository depends on one or more submodules. After cloning\nthis repository, from within the clone please run\n\n``git submodule update --init --recursive``\n\nto fetch all files contained in the submodules. This must be done before\nattempting to use the code in the clone. Issuing the command `git pull` will\nupdate the repository, but not the submodules. To update the clone and all its\nsubmodules simultaneously, run\n\n``git pull --recurse-submodules``.\n\nThe `ibcdfo` python package can be installed by setting up a terminal with the\ntarget python and pip pair and executing\n```\n> pushd ibcdfo_pypkg\n> python setup.py sdist\n> pip install dist/ibcdfo-<version>.tar.gz\n> popd\n```\nwhere `<version>` can be determined by looking at the output of the `sdist`\ncommand. The installation can be partially tested by executing\n```\n> python\n>>> import ibcdfo\n>>> ibcdfo.__version__\n<version>\n```\nwhere the output `<version>` should be identical to the value used during\ninstallation.\n\n## License\n\nAll code included in IBCDFO is open source, with the particular form of license contained in the top-level\nsubdirectories. If such a subdirectory does not contain a LICENSE file, then it is automatically licensed\nas described in the otherwise encompassing IBCDFO [LICENSE](/LICENSE).\n\n## Resources\n\nTo seek support or report issues, e-mail:\n\n * ``poptus@mcs.anl.gov``\n",
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