MIST: L0 Sparse Linear Regression with Momentum

In the current age of big data acquisition there has been an ever growing interest in sparse representations, which consists of representing, say, a noisy signal as a linear combination of very few components. This implies that the entire information in the signal can be approximately captured by a small number of components, which has huge benefits in analysis, processing and storage of high dimensional signals. As a result, sparse linear regression has been widely studied with many applications in signal and image processing, statistical inference and machine learning. Specific applications include compressed sensing, denoising, inpainting, deblurring, source separation, sparse image reconstruction, and signal classification, etc.