A Hybrid Algorithm for Convex Semidefinite Optimization
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.
Cite
Text
Laue. "A Hybrid Algorithm for Convex Semidefinite Optimization." International Conference on Machine Learning, 2012.Markdown
[Laue. "A Hybrid Algorithm for Convex Semidefinite Optimization." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/laue2012icml-hybrid/)BibTeX
@inproceedings{laue2012icml-hybrid,
title = {{A Hybrid Algorithm for Convex Semidefinite Optimization}},
author = {Laue, Sören},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/laue2012icml-hybrid/}
}