https://github.com/networks-learning/l1-ls.py

L1 regularized Least Squares minimization problem solver.
https://github.com/networks-learning/l1-ls.py

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L1 regularized Least Squares minimization problem solver.

• Host: GitHub
• URL: https://github.com/networks-learning/l1-ls.py
• Owner: Networks-Learning
• Created: 2015-08-15T20:04:14.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2016-10-12T13:20:44.000Z (about 8 years ago)
• Last Synced: 2024-09-16T13:48:06.919Z (2 months ago)
• Language: Python
• Size: 28.3 KB
• Stars: 17
• Watchers: 3
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst

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README

        
l1-ls

=====

|BuildStatus|

This is a large scale L1 regularized Least Square (L1-LS) solver written in

Python. The code is based on the MATLAB code made available on `Stephen Boyd's l1_ls page `_.

|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 `_

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/Networks-Learning/l1-ls.py.svg?branch=master

   :target: https://travis-ci.org/Networks-Learning/l1-ls.py

.. |L1LSProblem| image:: http://i.imgur.com/YB8JDTX.gif