Entrywise convergence of iterative methods for eigenproblems
Authors: Vasileios Charisopoulos, Austin R. Benson, Anil Damle
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our analysis with a practical stopping criterion and demonstrate its applicability via numerical experiments. In this section, we present a set of numerical experiments illustrating the results of our analysis in practice, as well as the advantages of the proposed stopping criterion. |
| Researcher Affiliation | Academia | Vasileios Charisopoulos Department of Operations Research & Information Engineering Cornell University Ithaca, NY 14853 EMAIL Austin R. Benson Department of Computer Science Cornell University Ithaca, NY 14853 EMAIL Anil Damle Department of Computer Science Cornell University Ithaca, NY 14853 EMAIL |
| Pseudocode | Yes | Algorithm 1 Subspace iteration Input: initial guess Q0 On,k, symmetric matrix A, iterations T for t = 1, 2, . . . , T do V (t) := AQt 1; Qt, Rt = qr(V (t)) QR decomposition end for return QT |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Table 1: Summary statistics of network datasets. Dataset Citation # nodes # edges CA-HEPPH [32] 11204 117649 CA-ASTROPH 17903 197031 GEMSEC-FACEBOOK-ARTIST [46] 50515 819306 COM-DBLP [55] 317080 1049866 COM-LIVEJOURNAL 3997962 34681189 |
| Dataset Splits | No | The paper describes generating synthetic data and using real-world graph datasets but does not provide specific details on training, validation, or test splits (e.g., percentages, counts, or a standard split reference). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8'). |
| Experiment Setup | No | The paper mentions that 'The supplementary material contains more details about the implementation and the experimental setup.' but does not include specific hyperparameters, training configurations, or system-level settings in the main text. |