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1
Intro
2
Compressive sensing: sparse signal recovery
3
Design problem
4
Parameters
5
Two approaches
6
Sparse matrices: Expander graphs
7
Applications: From polynomials to iPads
8
Definitions
9
Metric Repair Formally
10
Traditional Techniques
11
Three Repair Scenarios: Constrain P
12
Increase Only Metric Repair: Algorithms
Description:
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.

Sparse Matrices in Sparse Analysis - Anna Gilbert

Institute for Advanced Study
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