Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

*Cho-jui Hsieh, Arindam Banerjee, Inderjit Dhillon, Pradeep Ravikumar*

In this paper, we consider the $\ell_1$ regularized sparse inverse covariance matrix estimation problem with a very large number of variables. Even in the face of this high dimensionality, and with limited number of samples, recent work has shown this estimator to have strong statistical guarantees in recovering the true structure of the sparse inverse covariance matrix, or alternatively the underlying graph structure of the corresponding Gaussian Markov Random Field. Our proposed algorithm divides the problem into smaller sub-problems, and uses the solutions of the sub-problems to build a good approximation for the original problem. We derive a bound on the distance of the approximate solution to the true solution. Based on this bound, we propose a clustering algorithm that attempts to minimize this bound, and in practice, is able to find effective partitions of the variables. We further use the approximate solution, i.e., solution resulting from solving the sub-problems, as an initial point to solve the original problem, and achieve a much faster computational procedure. As an example, a recent state-of-the-art method, QUIC requires 10 hours to solve a problem (with 10,000 nodes) that arises from a climate application, while our proposed algorithm, Divide and Conquer QUIC (DC-QUIC) only requires one hour to solve the problem.

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