Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
Authors: Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show via numerical studies that the proposed estimator outperforms other popular methods for estimating Gaussian graphical models. |
| Researcher Affiliation | Collaboration | 1Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada 2School of Statistics, University of Minnesota, Minneapolis, MN, USA 3Tencent AI Lab, Tencent Technology, Shenzhen, China. |
| Pseudocode | Yes | Algorithm 1 A sequential convex approximation for the graphical nonconvex optimization. |
| Open Source Code | No | The paper states that 'Algorithm 1 can be implemented using existing R packages such as glasso.' but does not provide any link or explicit statement about releasing its own source code. |
| Open Datasets | No | The paper states, 'Finally, we generate the data according to X(1), . . . , X(n) i.i.d. N(0, Σ),' indicating that synthetic data was used, not a publicly available dataset. |
| Dataset Splits | No | The paper describes generating synthetic data and evaluating methods, but it does not specify any training, validation, or test dataset splits or cross-validation setups. It states, 'We present the results averaged over 100 data sets for each of the two simulation settings'. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, memory, or specific computing infrastructure used for the experiments. |
| Software Dependencies | No | The paper mentions that 'Algorithm 1 can be implemented using existing R packages such as glasso,' but it does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For our proposal, we consider T = 4 iterations with the SCAD penalty proposed by Fan & Li (2001)... In all of our simulation studies, we pick γ = 2.1. |