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Marie Michenková - A Brief Introduction to Compressed Sensing and Low-Rank Matrix Completion Problems

Date: 22/10/2013 12:20
Place: Seminární místnost KNM

Both compressed sensing and low-rank matrix recovery are recent 
and quickly developing sampling theories. In compressed sensing, 
the aim is to recover a sparse solution to an underdetermined 
system of equations. In low-rank matrix completion, we recover 
a low-rank data matrix from sampling of its entries. In the talk I will 
give a brief introduction to both theories and will explain the relation
 between them. If the time allows, I will also present some standard 
approaches to handle these problems.