Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Local and Global Linear Convergence of General Low-Rank Matrix Recovery Problems
Authors: Yingjie Bi, Haixiang Zhang, Javad Lavaei10129-10137
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct numerical experiments to demonstrate the behavior of the perturbed gradient descent algorithm for solving low-rank matrix recovery problems. |
| Researcher Affiliation | Academia | 1 Industrial Engineering and Operations Research, University of California, Berkeley 2 Department of Mathematics, University of California, Berkeley |
| Pseudocode | Yes | Algorithm 1 in Appendix C |
| Open Source Code | No | The paper does not provide concrete access to source code for the described methodology. |
| Open Datasets | No | The paper describes data generation processes (e.g., 'each entry of Ai is independently generated from the standard Gaussian distribution', 'ground truth M = XXT or M = UV T is generated randomly') rather than using a publicly available dataset with a specific link or citation. |
| Dataset Splits | No | The paper does not provide specific dataset split information (training, validation, test) for reproducibility, as data is generated for experiments rather than split from a pre-existing dataset. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For the symmetric problem (4), we ο¬rst generate 10^4 random matrices X Rn 2r with each entry independently selected from the standard Gaussian distribution... The ground truth M = XXT or M = UV T is generated randomly with each entry of X or (U, V ) independently selected from the standard Gaussian distribution. The initial point is generated in the same way. Figure 1(a) A symmetric linear problem with r = 1, n = 40, p = 120... (b) An asymmetric linear problem with r = 5, n = 10, m = 8, p = 220... (c) The 1-bit matrix recovery problem with r = 5, n = 10. (d) The 1-bit matrix recovery problem with r = 2, n = 600. |