Compressed Sensing and Sparse Recovery - 525.628

In recent years, compressed sensing (CS) has attracted considerable attention in areas of applied mathematics, computer science, and electrical engineering by suggesting that it may be possible to surpass the traditional limits of sampling theory. CS builds upon the fundamental fact that we can represent many signals using only a few non-zero coe?cients in a suitable basis or dictionary. Optimization can then enable recovery of such signals from very few measurements. Beautiful theoretical results show that structured signals, such as sparse vectors and low-rank matrices, can be recovered from relatively small sets of linear observations. These results raise intriguing possibilities for addressing engineering problems in signal and image processing, and beyond. The goal of this course is to provide students with the theoretical understanding, algorithmic tools, and implementation experience needed to use these tools to solve problems in their own area of interest, or even to begin doing novel work in this area.