Forward — Backward greedy algorithms for signal demixing

Signal demixing arises in many applications. Common among these are the separation of sparse and low rank components in image and video processing, sparse and group sparse models in multitask learning and spikes and sinusoids in source separation problems. For specific problems of interest, many methods exist to perform recovery, but an approach that generalizes to arbitrary notions of simplicity has not been forthcoming. We propose a framework for signal demixing when the components are defined to be simple in a fairly arbitrary manner. Our method remains computationally simple and can be used in a variety of practical applications.