Sparse Matrices in Sparse Analysis - Anna Gilbert

Explore the intricacies of sparse matrices in sparse analysis through this comprehensive lecture by Anna Gilbert from the University of Michigan. Delve into compressive sensing and sparse signal recovery, examining the design problem and its parameters. Discover two distinct approaches, with a focus on sparse matrices and expander graphs. Investigate applications ranging from polynomials to iPads, and learn about metric repair, including formal definitions and traditional techniques. Analyze three repair scenarios, including constraining P, and explore algorithms for increase-only metric repair. Gain valuable insights into this advanced mathematical topic presented at the Institute for Advanced Study's Members' Seminar.

Intro
Compressive sensing: sparse signal recovery
Design problem
Parameters
Two approaches
Sparse matrices: Expander graphs
Applications: From polynomials to iPads
Definitions
Metric Repair Formally
Traditional Techniques
Three Repair Scenarios: Constrain P
Increase Only Metric Repair: Algorithms