convex geometry

Monday, February 15, 2016 - 2:25pm - 3:25pm
Arnaud Marsiglietti (University of Minnesota, Twin Cities)
There are several relationships between the Information theory and Convex Geometry that have been highlighted in the late 80's, notably through the work of Costa, Cover, Dembo and Thomas. In this talk, we will review some of these relationships and discuss recent developments surrounding them.
Thursday, January 28, 2016 - 3:15pm - 4:05pm
Weiyu Xu (The University of Iowa)
In this talk, we explore the performance limits of recovering structured signals from low-dimensional linear projections, using tools from high dimensional convex geometry. In particular, we focus on two signal reconstruction applications: a total variation minimization for recovering gradient-sparse signals and a low-rank Hankel matrix completion for super-resolution of spectrally sparse signals. Using the tool of Gaussian width, we obtain counter-intuitive performance bounds on the sample complexity for these two applications.
Subscribe to RSS - convex geometry