Mathematical Introduction to Compressive Sensing

Department: 
MATH
Course Number: 
8803-LAC
Hours - Lecture: 
3
Hours - Lab: 
0
Hours - Recitation: 
0
Hours - Total Credit: 
3
Typical Scheduling: 
no regular schedule

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

Prerequisites: 

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.