A Hybrid Algorithm for Convex Semidefinite Optimization
  We present a hybrid algorithm for optimizing a convex, smooth function over the cone of positive semidefinite matrices. Our algorithm converges to the global optimal solution and can be used to solve general large-scale semidefinite programs and hence can be readily applied to a variety of machine learning problems. We show experimental results on three machine learning problems (matrix completion, metric learning, and sparse PCA) . Our approach outperforms state-of-the-art algorithms. 
Soeren Laue (Friedrich-Schiller-University)
cs.LG cs.DS cs.NA stat.ML