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It includes:\n\n- `ProxLQNSCORE` a limited-memory version of ProxQNSCORE of the Julia package\n- `ProxGGNSCORE`\n- `ProxNSCORE`\n- Smoothing and regularization (utility) functions\n\n## Installation\n\nInstall with pip:\n\n```sh\npip install pyscsopt\n```\n\n## Usage\n\nSee the [`examples/`](https://github.com/adeyemiadeoye/pySCSOpt/tree/main/examples) directory for a usage example.\n\nFor more information on how to set up problems (especially choosing regularizers), see Julia's [SelfConcordantSmoothOptimization.jl](https://github.com/adeyemiadeoye/SelfConcordantSmoothOptimization.jl).\n\n## Tests\n\nRun tests with:\n\n```sh\npytest pyscsopt/test/\n```\n\n## Citation\n\nIf you use this package for research, please cite:\n\n```bibtex\n@article{adeoye2023self,\n  title={Self-concordant Smoothing for Large-Scale Convex Composite Optimization},\n  author={Adeoye, Adeyemi D and Bemporad, Alberto},\n  journal={arXiv preprint arXiv:2309.01781},\n  year={2024}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadeyemiadeoye%2Fpyscsopt","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadeyemiadeoye%2Fpyscsopt","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadeyemiadeoye%2Fpyscsopt/lists"}