Metadata-Version: 1.1
Name: l1ls
Version: 0.1.0
Summary: Python package for solving large scale L1 regularizedleast squares problems.
Home-page: https://github.com/musically-ut/l1-ls.py
Author: Utkarsh Upadhyay
Author-email: musically.ut@gmail.com
License: MIT
Description: l1-ls
        =====
        
        |BuildStatus|
        
        This is a large scale L1 regularized Least Square (L!-LS) solver written in
        Python. The code is based entirely on the MATLAB code made available on `Stephen Boyd's l1_ls page <http://stanford.edu/~boyd/papers/l1_ls.html>`_.
        
        |L1LSProblem|
        
        
        Installation
        ------------
        
        You can install the bleeding edge directly from the source:
        
        ::
        
            pip install git+https://github.com/musically-ut/l1-ls.py.git@master#egg=l1ls
        
        
        This package is also available on PyPi.
        
        ::
        
            pip install l1ls
        
        Usage
        -----
        
        The module exposes two functions:
        
        - ``l1ls(A, y, lmbda, x0=None, At=None, m=None, n=None, tar_gap=1e-3, quiet=False, eta=1e-3, pcgmaxi=5000)``, and,
        - ``l1ls_nonneg(A, y, lmbda, x0=None, At=None, m=None, n=None, tar_gap=1e-3, quiet=False, eta=1e-3, pcgmaxi=5000)``
        
        They can be used as follows:
        
        ::
        
            import l1ls as L
            import numpy as np
        
            A = np.array([[1, 0, 0, 0.5], [0, 1, 0.2, 0.3], [0, 0.1, 1, 0.2]])
            x0 = np.array([1, 0, 1, 0], dtype='f8')  # Original signal
            y = A.dot(x0)                            # noise free signal
            lmbda = 0.01                             # regularization parameter
            rel_tol = 0.01
        
            [x, status, hist] = L.l1ls(A, y, lmbda, tar_gap=rel_tol)
            # answer_x = np.array([0.993010, 0.00039478, 0.994096, 0.00403702])
        
        If your matrix ``A`` is sparse, pass it in `CSR format <http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html>`_
        format for best performance.
        
        Reference
        ---------
        
        - S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky. An
          Interior-Point Method for Large-Scale l1-Regularized Least Squares,
          (2007), IEEE Journal on Selected Topics in Signal Processing,
          1(4):606-617.
        
        .. |BuildStatus| image:: https://travis-ci.org/musically-ut/l1-ls.py.svg?branch=master
           :target: https://travis-ci.org/musically-ut/l1-ls.py
        
        .. |L1LSProblem| image:: http://i.imgur.com/YB8JDTX.gif
        
Keywords: L1 least-squares optimization
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Operating System :: OS Independent
