Campuses:

gradient descent

Tuesday, June 25, 2019 - 11:30am - 12:30pm
Henrik Schumacher (RWTH Aachen University)
(Joint work with Philipp Reiter.)
Monday, May 16, 2016 - 10:00am - 10:50am
Sujay Sanghavi (The University of Texas at Austin)
Local algorithms like gradient descent are widely used in non-convex optimization, typically with few guarantees on performance. In this talk we consider the class of problems given by

min_{U,V} f(UV’)

where f is a convex function on the space of matrices. The problem is non-convex, but “only” because we adopt the bi-linear factored representation UV’, with tall matrices U,V.
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