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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Sparse Optimisation Research Code
| Date | Stars |
|---|---|
| 2026-07-24 | 273 |
| 2026-07-25 | 273 |
| 2026-07-28 | 273 |
| 2026-07-30 | 273 |
| 2026-08-06 | 273 |
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[](https://github.com/bwohlberg/sporco) [](https://github.com/bwohlberg/sporco/blob/master/LICENSE) [](http://sporco.readthedocs.io/en/latest/?badge=latest) [](https://github.com/bwohlberg/sporco/actions/workflows/pytest.yml) [](https://codecov.io/gh/bwohlberg/sporco)\ [](https://badge.fury.io/py/sporco) [](https://pepy.tech/project/sporco) [](https://anaconda.org/conda-forge/sporco) [](https://anaconda.org/conda-forge/sporco)\ [](https://mybinder.org/v2/gh/bwohlberg/sporco-notebooks/master?filepath=index.ipynb) [](https://dx.doi.org/10.25080/shinma-7f4c6e7-001) # SParse Optimization Research COde (SPORCO) SPORCO is a Python package for solving optimisation problems with sparsity-inducing regularisation. These consist primarily of sparse coding and dictionary learning problems, including convolutional sparse coding and dictionary learning, but there is also support for other problems such as Total Variation regularisation and Robust PCA. The optimisation algorithms in the current version are based on the Alternating Direction Method of Multipliers (ADMM) or on the Proximal Gradient Method (PGM). If you use this software for published work, please [cite it](http://sporco.readthedocs.io/en/latest/overview.html#citing). # Documentation [Documentation](http://sporco.rtfd.io/) is available online, or can be built from the root directory of the source distribution by the command python setup.py build_sphinx in which case the HTML documentation can be found in the `build/sphinx/html` directory (the top-level document is `index.html`). Although the SPORCO package itself is compatible with Python 3.x, building the documention requires Python 3.3 or later due to the use of [Jonga](https://github.com/bwohlberg/jonga) to construct call graph images for the SPORCO optimisation class hierarchies. An overview of the package design and functionality is also available in > Brendt Wohlberg, [SPORCO: A Python package for standard and convolutional sparse representations](http://conference.scipy.org/proceedings/scipy2017/brendt_wohlberg.html), > in Proceedings of the 15th Python in Science Conference, (Austin, TX, USA), doi:10.25080/shinma-7f4c6e7-001, pp. 1--8, Jul 2017 # Usage Scripts illustrating usage of the package can be found in the `examples` directory of the source distribution. These examples can be run from the root directory of the package by, for example python examples/scripts/sc/bpdn.py To run these scripts prior to installing the package it will be necessary to first set the `PYTHONPATH` environment variable to include the root directory of the package. For example, in a `bash` shell export PYTHONPATH=$PYTHONPATH:`pwd` from the root directory of the package. [Jupyter Notebook](http://jupyter.org/) examples are also [available](https://github.com/bwohlberg/sporco-notebooks). These examples can be viewed online via [nbviewer](https://nbviewer.jupyter.org/github/bwohlberg/sporco-notebooks/blob/master/index.ipynb), or run interactively at [binder](https://mybinder.org/v2/gh/bwohlberg/sporco-notebooks/master?filepath=index.ipynb). # Req
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4e12462afafe207f, topic:sparsity, readme:sparsity
matched fp:4e12462afafe207f, topic:cuda