Compressed Sensing and Related Topics

Catalog description

Introduction to the basic compressed sensing problems and methodologies, including the recovery of sparse vectors and low-rank matrices using methods based on convex optimization and approximate message passing. Unified theoretical framework for the analysis of certain CS problems, drawing upon ideas from statistical decision theory, high-dimensional convex geometry, information theory, convex optimization, message passing and variational inference with graphical models, and the replica method from statistical physics.