QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation
Authors: Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar
JMLR 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our method is superlinearly convergent, and present experimental results using synthetic and real-world application data that demonstrate the considerable improvements in performance of our method when compared to previous methods. [...] We summarize the experimental results in Section 5, where we compare the algorithm using both real data and synthetic examples from Li and Toh (2010). |
| Researcher Affiliation | Academia | Cho-Jui Hsieh EMAIL Matyas A. Sustik EMAIL Inderjit S. Dhillon EMAIL Pradeep Ravikumar EMAIL Department of Computer Sciences University of Texas at Austin Austin, TX 78712, USA |
| Pseudocode | Yes | Algorithm 1: QUadratic approximation for sparse Inverse Covariance estimation (QUIC overview) [...] Algorithm 2: QUadratic approximation for sparse Inverse Covariance estimation (QUIC) [...] Algorithm 3: General Block Quadratic Approximation method for Sparse Inverse Covariance Estimation |
| Open Source Code | Yes | Our software package QUIC with MATLAB and R interface1 is public available at http://www.cs.utexas.edu/~sustik/QUIC/. The QUIC R package is also available from CRAN. |
| Open Datasets | Yes | We use the real world biology data sets preprocessed by Li and Toh (2010) to compare the performance of our method with other state-of-the-art methods. |
| Dataset Splits | No | The paper describes generating synthetic data and using preprocessed real-world biology datasets, but it does not specify explicit training/test/validation splits, proportions, or methodologies for partitioning the data to reproduce experiments. |
| Hardware Specification | Yes | all experiments were executed on 2.83GHz Xeon X5440 machines with 32G RAM and Linux OS. |
| Software Dependencies | Yes | We have implemented QUIC in C++ with MATLAB interface, and all experiments were executed on 2.83GHz Xeon X5440 machines with 32G RAM and Linux OS. We use the latest version glasso 1.7 downloaded from http://www-stat.stanford.edu/~tibs/glasso/. |
| Experiment Setup | Yes | We set the augmented Lagrangian parameter to ρ = 50 and the over-relaxation parameter to α = 1.5. These parameters achieved the best speed on the ER data set. [...] We use the identity matrix as the initial point for QUIC, ADMM, SINCO, and IPM. [...] In the first set of experiments, we set the regularization parameter λ to be 0.5 [...] we set the number of coordinate descent steps to be αt for the t-th outer iteration, where α is a constant; we use α = 1/3 in our experiments. |