## About AMP-MMV

AMP-MMV is a Bayesian message passing algorithm that solves the multiple measurement vector (MMV) problem, in which a matrix of noisy measurements, **Y**, is acquired from a sparse signal matrix, **X**, through the linear measurement process **Y = AX + E****A** is a measurement matrix (typically with more columns than rows), and **E** is corrupting noise. Each column in **X** can represent the value of some time-varying signal at a particular instant in time, or can represent a time-invariant signal acquired in one of multiple channels. The unique feature of the MMV problem is the assumption that each column of **X** shares the same support, i.e., **X** is row-sparse.

- AMP-MMV works really quickly, especially in high-dimensions!
- Designed for cases with significant column correlations in
**X** - Model parameters learned automatically from data
- Performs near theoretical bounds for many problems
- Support for implicit matrix operators, e.g., FFTs

**Benefits of AMP-MMV:**

AMP-MMV has been implemented in MATLAB, and has been shown to work extremely quickly, requiring only simple matrix-vector products to perform its computations. Click here to try it out.