Metadata-Version: 2.1
Name: spgl1
Version: 0.0.1
Summary: 
        SPGL1: A solver for large-scale sparse reconstruction.
        
Home-page: https://github.com/drrelyea/SPGL1_python_port
Author: E. van den Berg, M. P. Friedlander (original MATLAB authors). David Relyea and contributors (python port).
Author-email: drrelyea@gmail.com
License: UNKNOWN
Description: # SPGL1: Spectral Projected Gradient for L1 minimization
        [![Build Status](https://travis-ci.org/drrelyea/spgl1.svg?branch=master)](https://travis-ci.org/drrelyea/spgl1)
        [![PyPI version](https://badge.fury.io/py/spgl1.svg)](https://badge.fury.io/py/spgl1)
        [![Documentation Status](https://readthedocs.org/projects/spgl1/badge/?version=latest)](https://spgl1.readthedocs.io/en/latest/?badge=latest)
        
        Original home page: http://www.cs.ubc.ca/labs/scl/spgl1/
        
        ## Introduction
        SPGL1 is a solver for large-scale one-norm
        regularized least squares.
        
        It is designed to solve any of the following three problems:
        
        1. Basis pursuit denoise (BPDN):
           ``minimize  ||x||_1  subject to  ||Ax - b||_2 <= sigma``,
        
        2. Basis pursuit (BP):
           ``minimize   ||x||_1  subject to  Ax = b``
         
        3. Lasso:
           ``minimize  ||Ax - b||_2  subject to  ||x||_1 <= tau``,
        
        The matrix ``A`` can be defined explicitly, or as an operator
        that returns both both ``Ax`` and ``A'b``.
        
        SPGL1 can solve these three problems in both the real and complex domains.
        
        ## Installation
        
        #### From PyPi
        
        If you want to use ``spgl1`` within your codes, install it in your
        Python environment by typing the following command in your terminal:
        
        ```
        pip install spgl1
        ```
        
        #### From Source
        
        First of all clone the repo. To install ``spgl1`` within your current
        environment, simply type:
        ```
        make install
        ```
        or as a developer:
        ```
        make dev-install
        ```
        
        To install ``spgl1`` in a new conda environment, type:
        ```
        make install_conda
        ```
        or as a developer:
        ```
        make dev-install_conda
        ```
        
        
        ## Getting started
        Examples can be found in the ``examples`` folder in the form of
        jupyter notebooks.
        
        ## Documentation
        The official documentation is built with Sphinx and hosted on
        [readthedocs](https://spgl1.readthedocs.io/en/latest/).
        
        
        ## References
        
        The algorithm implemented by SPGL1 is described in these two papers
        
        - E. van den Berg and M. P. Friedlander, "Probing the Pareto frontier
          for basis pursuit solutions", SIAM J. on Scientific Computing,
          31(2):890-912, November 2008
        
        - E. van den Berg and M. P. Friedlander, "Sparse optimization with
          least-squares constraints", Tech. Rep. TR-2010-02, Dept of Computer
          Science, Univ of British Columbia, January 2010
        
Keywords: algebra,inverse problems,large-scale optimization
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Mathematics
Description-Content-Type: text/markdown
