Exploiting sparsity and other structures of solutions has become a common task in various computational and engineer areas including signal/image processing, compressed sensing, statistics, machine learning, data mining, and so on. Sparsity not only makes it possible to reconstruct high-dimensional signals and discover its salient information from a small number of measurements, but also makes optimization faster and enables extremely large-scale computation. A large number of novel applications have emerged to take advantages of sparsity. Starting from some application problems with structured solutions, this course gives an overview of sparse optimization theory, algorithms, and several applications. In addition, it covers implementational aspects of large-scale, parallel, and distributed computation.
This short summer course gives an overview to sparse optimization with a focus on its high-performance computational methods.
Open questions, including difficult ones, will be assigned as homework and project assignments.
none, but lecture notes and papers will be posted before each class
3 credit hours.