The examples about this methods are under npgEx
folder with filenames
ended with Ex
, where the code can reproduce the figures appear in our
papers and reports. The algorithm implementations are under folder npg
,
which use the some utility functions under utils
folder.
- R. Gu and A. Dogandžić, "Projected Nesterov’s proximal-gradient algorithm for sparse signal recovery," IEEE Trans. Signal Process., vol. 65, no. 13, pp. 3510–3525, 2017. [DOI] [PDF]
Usually for regularized convex optimization problem with optional convex constraints, there is a regularization parameter u. As the value of u increases, the optimal signal shifts to a state that solely determined by the regularization terms, i.e., the enforced prior information. It is of interest to find out such a treshold U beyond which, the optimum converges to this final state.
The code utils/uBound.m
solves this U via ADMM for an arbitrary convex
likelihood function with convex constraints under l1-norm regularization in
a linear transformed domain. The examples in folder uBoundEx
include the
applications with DWT and (an)isotropic TV regularizations.
- R. Gu and A. Dogandžić, (Feb. 2017). Upper-Bounding the Regularization Constant for Convex Sparse Signal Reconstruction. arXiv: 1702.07930 [stat.CO].
To install this package, first download the repository by running
git clone https://github.com/isucsp/pnpg.git
after downloading, from MATLAB change your current folder to pnpg/
and execute setupPath.m
to add necessary paths to the environment.
For Windows
, you may need to have Visual Studio or other C/C++ compilers
installed to compile some C code while calling setupPath.m
.
For UNIX
, you may need to have gcc installed.
For the 3rd party softwares that are used, please refer to
getOthersCode.sh
in how to get them.
The comments in some of *.m
files may contain greek letters, which
are UTF-8
encoded. Please open in an appropriately configured text
editor.