Mathematical Introduction to Compressive Sensing

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no regular schedule

Special topics course on Mathematical Introduction to Compressive Sensing, by Michael Lacey, offered Fall 2016.


MATH 4305 and familiarity with Gaussian random variables and stochastic processes

Course Text: 

A Mathematical Introduction to Compressive Sensing. Authors: Foucart, Simon, Rauhut, Holger (GaTech students can get an electronic copy from the library website. A free GT-network download) 

Topic Outline: 

Compressive sensing concerns the analysis of signals which are 'compressive' or 'sparse' in that they admit a description that is  substantially smaller than their presentation. Topics to be covered include

Sparse solutions to under determined systems of equations
Basic algorithms, including ell^1 minimization
Tools of probability theory
Random methods in compressive sensing
Restricted Isometry Property
Non-linear methods
Will assume familiarity with linear algebra, and familiarity with probability theory, convex analysis and some statistical analysis will assumed from time to time.