## About DCS-AMP

DCS-AMP is a Bayesian message passing algorithm that solves the dynamic compressed sensing (DCS) problem, in which a time-varying vector of noisy measurements, **y**(t), is acquired from a time-varying sparse signal vector, **x**(t), through the linear measurement process **y**(t) = **A**(t)**x**(t) + **e**(t)**A**(t) is a measurement matrix (typically with more columns than rows), and **e**(t) is corrupting noise. The unique feature of the DCS problem is the assumption that the support (i.e., the locations of the non-zero entries) of **x**(t) varies slowly over time.

- DCS-AMP works really quickly, especially in high-dimensions!
- Performs soft estimation and support detection
- Model parameters learned automatically from data
- Performs near theoretical bounds for many problems
- Support for implicit matrix operators, e.g., FFTs

**Benefits of DCS-AMP:**

DCS-AMP 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.