I released the code for the paper “A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation” that was presented in NIPS 2014. The algorithm includes a flag that enables the multilevel acceleration. This flag is very useful for large-scale problems on the thousands-millions of variables. The code runs in Matlab and includes some functions in C that require compilation. Also, it calls functions from METIS 5.0.2 to partitioning the neighbors in every sweep. The released version was tested on Windows, although it should work on other platforms as well.
You are welcome to try it and contact me with any comment you may have. I would like to know if somebody managed to run it in linux or mac.
Today, I found that the work “A Block-Coordinate Descent Approach for Large-Scale Sparse Inverse Covariance Estimation” joint with Eran Treister was published in the NIPS 2014 proceedings website. I will publish the algorithm code for this work and the Multilevel framework in a few days.
Hope that you enjoy it and please send me your comments!
A few days ago, I’ve received the notification about the acceptance to NIPS 2014 of the work I submitted with my friend and colleague Eran Treister back in June. The NIPS 2014 conference will be held in Montreal, Canada during December 8th and 11th. Our work is about a new algorithm to solve the Sparse Inverse Covariance Estimation problem in high dimensions, such that the memory is a limitation factor. In the work we show that the algorithm is faster than the previous methods in thousands to millions of variables, and that the algorithm is capable of running in a single server with 64GB because of its reduced memory usage.