Faster Projection-free Convex Optimization over the Spectrahedron

  title={Faster Projection-free Convex Optimization over the Spectrahedron},
  author={Dan Garber},
Minimizing a convex function over the spectrahedron, i.e., the set of all d ⇥ d 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 to compute expensive matrix decompositions. An alternative is the conditional gradient method (aka Frank-Wolfe algorithm) that regained much interest in recent years… CONTINUE READING
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