SPGL1: A solver for large-scale sparse reconstruction.
SPGL1: Spectral Projected Gradient for L1 minimization
Original home page: http://www.cs.ubc.ca/labs/scl/spgl1/
SPGL1 is a solver for large-scale one-norm regularized least squares.
It is designed to solve any of the following three problems:
Basis pursuit denoise (BPDN):
minimize ||x||_1 subject to ||Ax - b||_2 <= sigma,
Basis pursuit (BP):
minimize ||x||_1 subject to Ax = b
minimize ||Ax - b||_2 subject to ||x||_1 <= tau,
A can be defined explicitly, or as an operator
that returns both both
SPGL1 can solve these three problems in both the real and complex domains.
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
First of all clone the repo. To install
spgl1 within your current
environment, simply type:
or as a developer:
spgl1 in a new conda environment, type:
or as a developer:
Examples can be found in the
examples folder in the form of
The official documentation is built with Sphinx and hosted on readthedocs.
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
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