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I added the link to the paper “A Multilevel Framework for Sparse Optimization With Application to Inverse Covariance Estimation and Logistic Regression” soon to appear in SIAM Scientific Computing (SISC) journal. The paper describes a method that accelerates sparse optimization methods that use L1 regularization to achieve sparse solution. We show how to apply this method to the sparse inverse covariance method (also known as GLASSO) and the L1-regularized logistic regression.
Last week, I received the notice that the work with Eran Treister and Irad Yavneh was accepted in the optimization workshop at NIPS 2014. This is a follow up work the sparse inverse covariance work, where we present an acceleration framework based on multilevel techniques. The framework reduces the number of computations needed by defining an hierarchy of levels and updating a subset of the active set of non-zero elements. We tested the framework on QUIC and on BCD-IC algorithms with very interesting results, in particular for large-scale problems where the timings are reduced up to 10x.
See you at NIPS 2014 and in the OPT 2014 workshop.