CompressedSensing

This package contains several useful algorithms for compressed sensing, multiple measurement vectors, and sparse blind source separation.

• Examples
• Functions

Available Algorithms

SMV - Single Measurement Vectors

• IRLS - Equality constrained Iteratively Rewieghted Least Squares Lp Minimization 1
• UIRLS - Unconstrained Iteratively Reweighted Lease Squares Lp Minimization 1

MMV - Multiple Measurement Vectors

• ZAP - Zeropoint Attractor 2

BSS - Sparse Blind Source Separation

• nGMCA - Sparse non-negative Blind Source Separation 3

Quantifying Sparsity

• GI - Absolute Gini Index 4
• Coherence - Measuring the coherence of a measurement matrix by the definitions commonly used 5