## GPU Accelerate Greedy Algorithms for Compressed Sensing

## New Release: GAGA 1.2.0, October 2015

Welcome to GAGA, a software package for solving large compressed sensing problems with millions of unknowns in fractions of a second by exploiting the power of graphics
processing units. The current release GAGA 1.2.0 consists of ten greedy algorithms using five matrix ensembles and six algorithms tailored for use with expander matrices. This release is set to compile as
Matlab executables to enhance your compressed sensing research and applications, as well as assist you in case you need Computer Science homework help. A user guide is
available for download detailing the capabilities including simple implementations for large-scale testing at problem sizes previously too computationally expensive for extensive testing.

The current version, GAGA 1.2.0, contains ten greedy algorithms for compressed sensing with three clases of matrix multiplication, generic dense matrices,
sparse matrices, and the subsampled discrete cosine transform. New to GAGA 1.2.0 are six algorithms specificaly designed for the compressed sensing problem with expander matrices. For large-scale testing, there are a total of five randomly generated matrix ensembles and three randomly generated sparse vector ensembles.
For applications, the algorithms are equipped to employ any dense matrix and any sparse matrix in COO format (the default in Matlab). GAGA provides massive acceleration with up to 70x speed-ups
in the algorithms' subroutines over a CPU based matlab implementation. For large scale testing, the GPU based random problem generation can offer up to 1600x acceleration.

## Greedy AlgorithmsConjugate Gradient Iterative Hard Thresholding (3 variants)
Normalized Iterative Hard Thresholding Fast Iterative Hard Thresholding Iterative Hard Thresholding Hard Thresholding Pursuit CSMPSP: CoSaMP/Subspace Pursuit 1-ALPS(2) Thresholding |
## Expander AlgorithmsSerial L0
Parallel L0 Expander Recovery Sparse Matching Pursuit Sequential Sparse Matching Pursuit Parallel LDDSR |

## Random Matrix EnsemblesDense Gaussian Matrices
Dense Binary Matrices Sparse Binary Matrices Sparse Expander Matrices Subsampled Discrete Cosine Transform |
## Random Sparse Vector EnsemblesSparse Binary Vectors
Sparse Gaussian Vectors Sparse Uniform Vectors |