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        "author": "E. van den Berg, M. P. Friedlander (original MATLAB authors). David Relyea and contributors (python port).",
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            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "# SPGL1: Spectral Projected Gradient for L1 minimization\n[![Build Status](https://travis-ci.org/drrelyea/spgl1.svg?branch=master)](https://travis-ci.org/drrelyea/spgl1)\n[![PyPI version](https://badge.fury.io/py/spgl1.svg)](https://badge.fury.io/py/spgl1)\n[![Documentation Status](https://readthedocs.org/projects/spgl1/badge/?version=latest)](https://spgl1.readthedocs.io/en/latest/?badge=latest)\n\nOriginal home page: http://www.cs.ubc.ca/labs/scl/spgl1/\n\n## Introduction\nSPGL1 is a solver for large-scale one-norm\nregularized least squares.\n\nIt is designed to solve any of the following three problems:\n\n1. Basis pursuit denoise (BPDN):\n   ``minimize  ||x||_1  subject to  ||Ax - b||_2 <= sigma``,\n\n2. Basis pursuit (BP):\n   ``minimize   ||x||_1  subject to  Ax = b``\n\n3. Lasso:\n   ``minimize  ||Ax - b||_2  subject to  ||x||_1 <= tau``,\n\nThe matrix ``A`` can be defined explicitly, or as an operator\nthat returns both both ``Ax`` and ``A'b``.\n\nSPGL1 can solve these three problems in both the real and complex domains.\n\n## Installation\n\n#### From PyPi\n\nIf you want to use ``spgl1`` within your codes, install it in your\nPython environment by typing the following command in your terminal:\n\n```\npip install spgl1\n```\n\n#### From Source\n\nFirst of all clone the repo. To install ``spgl1`` within your current\nenvironment, simply type:\n```\nmake install\n```\nor as a developer:\n```\nmake dev-install\n```\n\nTo install ``spgl1`` in a new conda environment, type:\n```\nmake install_conda\n```\nor as a developer:\n```\nmake dev-install_conda\n```\n\n\n## Getting started\nExamples can be found in the ``examples`` folder in the form of\njupyter notebooks.\n\n## Documentation\nThe official documentation is built with Sphinx and hosted on\n[readthedocs](https://spgl1.readthedocs.io/en/latest/).\n\n\n## References\n\nThe algorithm implemented by SPGL1 is described in these two papers\n\n- E. van den Berg and M. P. Friedlander, \"Probing the Pareto frontier\n  for basis pursuit solutions\", SIAM J. on Scientific Computing,\n  31(2):890-912, November 2008\n\n- E. van den Berg and M. P. Friedlander, \"Sparse optimization with\n  least-squares constraints\", Tech. Rep. TR-2010-02, Dept of Computer\n  Science, Univ of British Columbia, January 2010\n\n\n",
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