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.