Regularization Paths with Guarantees for Convex Semidefinite Optimization
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:432-439, 2012.
We devise a simple algorithm for computing an approximate solution path for parameterized semidefinite convex optimization problems that is guaranteed to be epsilon-close to the exact solution path. As a consequence, we can compute the entire regularization path for many regularized matrix completion and factorization approaches, as well as nuclear norm or weighted nuclear norm regularized convex optimization problems. This also includes robust PCA and variants of sparse PCA. On the theoretical side, we show that the approximate solution path has low complexity. This implies that the whole solution path can be computed efficiently. Our experiments demonstrate the practicality of the approach for large matrix completion problems.