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SUMMARY:Matrix Means\, Distances\, Kernels\, and Geometric Optimization - 
 Suvrit Sra (Max-Planck Institute for Intelligent Systems and CMU)
DTSTART:20140317T113000Z
DTEND:20140317T123000Z
UID:TALK51478@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:I will talk about a new distance function on hermitian positiv
 e definite (hpd) matrices that takes inspiration from both differential ge
 ometry and convex optimization. This distance function originally arose in
  a computer-vision application\, but since then it took on a life of its o
 wn that is interesting enough to talk about.\n\nIn my talk\, I will briefl
 y describe the original application\, followed by key results and wider ma
 thematical connections of this distance function\, e.g.\, to the theory of
  kernel functions on symmetric spaces\; matrix theory\; nonlinear matrix e
 quations\; symmetric polynomials in noncommutative variables\, quantum inf
 ormation theory\, and the expanding area of geometric optimization with hp
 d matrices. I will also mention a few challenging open problems.
LOCATION:Engineering Department\, CBL Room BE-438
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