Entrywise convergence of iterative methods for eigenproblems
Authors: Vasileios Charisopoulos, Austin R. Benson, Anil Damle
NeurIPS 2020 | Conference 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 vc333@cornell.edu Austin R. Benson Department of Computer Science Cornell University Ithaca, NY 14853 arb@cs.cornell.edu Anil Damle Department of Computer Science Cornell University Ithaca, NY 14853 damle@cornell.edu |
| 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. |