MOTIVATION AND GOALS

When retrieving information from given data, one crucial point is the choice of an adequate data model. One model that stands out as quite simple yet very important is the model which is based on sparse description of the data. This model has attracted huge interest in the past decade resulting in many breakthroughs in several areas ranging from pure and applied mathematics to computational sciences as well as many applications. An important aspect of the notion of sparsity relates to reducing resources needed for sensing an unknown signal. The ability of simple algorithms to solve the seemingly intractable problem of finding the sparsest solution of a severely underdetermined linear system of equations, under realistic conditions, leads immediately to new signal reconstruction methods that are successful with surprisingly few measurements. This field has been referred to in the literature as Compressed Sensing. The concepts of sparse signal models and compressive sampling have not only important implications in the field of analog-to-digital sampling theories. This new direction of compressive sampling seems to be very powerful also in other fields. For instance, first attempts exist in elaborating a theory for the application of compressed sensing in the field of numerical schemes for solving ill- and well-posed operator equations. To discuss and investigate the impact, current state of the art and possible directions and potential of efficient sensing strategies is the major request of this workshop.