Overview
pdNCG (primaldual Newton Conjugate Gradients) is a MATLAB
implementation for the solution of unconstrained l1regularized problems.
For example, Machine Learning problems, such as l1regularized leastsquares
and logistic regression, Compressed Sensing problems, such as l1synthesis,
l1analysis and isotropic totalvariation. The solver is memoryless, it
requires only matrixvector product operations, hence it is appropriate for
largescale instances.
Download
 Source code:
Latest version (v3)
Previous version (v2)
Previous version (v1)
 Additional scripts: scripts that reproduce all experiments in the paper "A Preconditioner for a PrimalDual Newton Conjugate Gradients Method for Compressed Sensing Problems" (Version 3 of pdNCG has been used for these experiments)
 Additional scripts: scripts that reproduce all experiments in the paper "A SecondOrder Method for Compressed Sensing Problems with Coherent and Redundant Dictionaries" (Version 1 of pdNCG has been used for this experiment)
 Additional scripts: scripts that reproduce all experiments in the paper "A SecondOrder Method for Strongly Convex L1Regularization Problems" (version 2 of pdNCG has been used for these experiments)
License
pdNCG or primaldual Newton Conjugate Gradients
Copyright (C) 2014, Ioannis Dassios, Kimon Fountoulakis, Robert Mansel Gower
and Jacek Gondzio
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
References

K. Fountoulakis and J. Gondzio:
A SecondOrder Method for StronglyConvex L1Regularization Problems,
Technical Report ERGO14005, School of Mathematics, 2014.
Published in Mathematical Programming.

I. Dassios, K. Fountoulakis and J. Gondzio:
A SecondOrder Method for Compressed Sensing Problems with Coherent and Redundant Dictionaries,
Technical Report ERGO14007, School of Mathematics, 2014.

I. Dassios, K. Fountoulakis and J. Gondzio:
A Preconditioner for a PrimalDual Newton Conjugate Gradients Method for Compressed Sensing Problems,
Technical Report ERGO14021, School of Mathematics, 2014.
Published in SIAM Journal on Scientific Computing.