Faster Projection-free Convex Optimization over the Spectrahedron

NeurIPS 2016 Dan Garber

Minimizing a convex function over the spectrahedron, i.e., the set of all positive semidefinite matrices with unit trace, is an important optimization task with many applications in optimization, machine learning, and signal processing. It is also notoriously difficult to solve in large-scale since standard techniques require expensive matrix decompositions... (read more)

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