Estimating Change Points in High-Dimensional Linear Regression using Approximate Message Passing

The authors propose an Approximate Message Passing (AMP) algorithm to efficiently estimate the signals and change point locations in high-dimensional linear regression. They provide an exact asymptotic characterization of the algorithm's performance and show how to quantify uncertainty in the estimates.