Implementations of Algorithms for sparse optimization / sparse signal recovery

Broadly speaking, the algorithms here do the following:
Given existence of sparse solution x to Ax = b, recover sparse approximation 
\tilde{x} close to x given matrix A and vector b (or the noisy variant of b) 

Targeting large sparse matrices on multi-core/parallel architectures

We develop three types of codes
1. Matlab - simple to read and use, for smaller systems and illustration purposes
2. GPU - for use with NVIDIA graphics cards, targeting medium size applications 
3. MPI - targeting large scale applications

==== Generating Data ====

To generate data for different matrix/signal types use the script 
system_data_generators/driver_generate_system_data.m with Matlab 
or Octave. This can generate a number of different systems types 
to test with. Users can also regulate, noise, column norm variations, etc.

to clean up data, image files use:
rm -rf data/*
rm -rf images/*


==== Using Matlab/Octave codes ====

To test some of the algorithms in Matlab run the script 
codes_matlab/driver_runner_algs1.m . Notice that this driver 
can run several trials over a given set of systems with 
different algorithms (accordingly, the same 
number of instances must be generated via driver_generate_system_data.m 
prior to running the script). 
The script codes_matlab/driver_plotter_algs1.m can then plot median 
and 1st/3rd quartile quantities collected by the driver. 


==== Using GPU codes ====

For this, one must have an NVIDIA cuda capable card, install CUDA and 
CUSP (http://cusplibrary.github.io/). Then see the codes in 
codes_nvidia_cuda/ folder. The code sparse_algs_cuda1.c loads a matrix 
and vectors previously generated with driver_generate_system_data.m and 
runs a sparse optimization algorithm. Typically the runtime for the same 
number of iterations is significantly lower than for Matlab.
More algorithms to be added to GPU.


==== Using MPI codes ====

You must install MPICH (or another version of MPI) and PETSc 
(typically, PETSc pulls and compiles MPICH automatically).
For installation instructions, see: http://www.mcs.anl.gov/petsc/
This is under development and you must supply your own matrices in 
PETSc format. See code petsc_test_l1.c in codes_mpi_petsc/ . Matrices 
and vectors are submitted using command line arguments pointing to location 
of binary files following the default binary format:
http://www.mcs.anl.gov/petsc/petsc-current/docs/manualpages/Mat/MatLoad.html   
 

Sergey Voronin
2015