Local and Global Linear Convergence of General Low-Rank Matrix Recovery Problems
Authors: Yingjie Bi, Haixiang Zhang, Javad Lavaei10129-10137
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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 first 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. |