{
    "info": {
        "author": "Utkarsh Upadhyay",
        "author_email": "musically.ut@gmail.com",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5"
        ],
        "description": "l1-ls\n=====\n\n|BuildStatus|\n\nThis is a large scale L1 regularized Least Square (L1-LS) solver written in\nPython. The code is based on the MATLAB code made available on `Stephen Boyd's l1_ls page <http://stanford.edu/~boyd/papers/l1_ls.html>`_.\n\n|L1LSProblem|\n\n\nInstallation\n------------\n\nYou can install the bleeding edge directly from the source:\n\n::\n\n    pip install git+https://github.com/musically-ut/l1-ls.py.git@master#egg=l1ls\n\n\nThis package is also available on `PyPi <https://pypi.python.org/pypi/l1ls>`_.\n\n::\n\n    pip install l1ls\n\nUsage\n-----\n\nThe module exposes two functions:\n\n- ``l1ls(A, y, lmbda, x0=None, At=None, m=None, n=None, tar_gap=1e-3, quiet=False, eta=1e-3, pcgmaxi=5000)``, and,\n- ``l1ls_nonneg(A, y, lmbda, x0=None, At=None, m=None, n=None, tar_gap=1e-3, quiet=False, eta=1e-3, pcgmaxi=5000)``\n\nThey can be used as follows:\n\n::\n\n    import l1ls as L\n    import numpy as np\n\n    A = np.array([[1, 0, 0, 0.5], [0, 1, 0.2, 0.3], [0, 0.1, 1, 0.2]])\n    x0 = np.array([1, 0, 1, 0], dtype='f8')  # Original signal\n    y = A.dot(x0)                            # noise free signal\n    lmbda = 0.01                             # regularization parameter\n    rel_tol = 0.01\n\n    [x, status, hist] = L.l1ls(A, y, lmbda, tar_gap=rel_tol)\n    # answer_x = np.array([0.993010, 0.00039478, 0.994096, 0.00403702])\n\nIf your matrix ``A`` is sparse, pass it in `CSR format <http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html>`_\nformat for best performance.\n\nReference\n---------\n\n- S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky. An\n  Interior-Point Method for Large-Scale l1-Regularized Least Squares,\n  (2007), IEEE Journal on Selected Topics in Signal Processing,\n  1(4):606-617.\n\n.. |BuildStatus| image:: https://travis-ci.org/musically-ut/l1-ls.py.svg?branch=master\n   :target: https://travis-ci.org/musically-ut/l1-ls.py\n\n.. |L1LSProblem| image:: http://i.imgur.com/YB8JDTX.gif",
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        "keywords": "L1 least-squares optimization",
        "license": "MIT",
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        "name": "l1ls",
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