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A nearly condition-free fast algorithm for Gaussian graphical model recovery

Statistics GIDP Colloquium

A nearly condition-free fast algorithm for Gaussian graphical model recovery
Series: Statistics GIDP Colloquium
Location: Math 501
Presenter: Hui Zou, University of Minnesota

Many methods have been proposed for estimating Gaussian graphical model. The most popular ones are the graphical lasso and neighborhood selection, because the two are computational very efficient and have some theoretical guarantees. However, their theory for graph recovery requires some very stringent structure assumption (a.k.a. the irrepresentable condition). We argue that replacing the lasso penalty in these two methods with a non-convex penalty does not fundamentally remove the theoretical limitation, because another structure condition is required. As an alternative, we propose a new algorithm for graph recovery that is very fast and easy to implement and enjoys strong theoretical properties under basic sparsity assumptions.