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arXiv · 1206.4608

A Hybrid Algorithm for Convex Semidefinite Optimization

Soeren Laue
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Abstract

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.

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Soeren Laue. 2012-06-18. A Hybrid Algorithm for Convex Semidefinite Optimization. https://arxiv.org/abs/1206.4608

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