A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
Authors: Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide simulation results of the primal-dual based method, as discussed in Section 5, for matrix completion and one-bit matrix completion. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA. 2Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA. |
| Pseudocode | Yes | Algorithm 1 Augmented Lagrangian Method |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | We generate the observed data matrix according to the uniform observation model (2.3). In particular, the unknown low-rank matrix X Rd1 d2 is generated via X = U V , where each entry of U Rd1 r and V Rd2 r is generated independently from standard Gaussian distribution, and we scale them to ensure max{ U 2, , V 2, } ", where " = 2. |
| Dataset Splits | No | The paper describes data generation and various experimental settings but does not provide specific dataset split information (e.g., percentages or counts for training, validation, and test sets) as required for reproduction. |
| Hardware Specification | No | The paper mentions that algorithms are implemented in Matlab but provides no specific details about the hardware (e.g., CPU, GPU models) used for experiments. |
| Software Dependencies | No | All of the aforementioned algorithms are implemented in Matlab |
| Experiment Setup | Yes | We generate the observed data matrix according to the uniform observation model (2.3). In particular, the unknown low-rank matrix X Rd1 d2 is generated via X = U V , where each entry of U Rd1 r and V Rd2 r is generated independently from standard Gaussian distribution, and we scale them to ensure max{ U 2, , V 2, } ", where " = 2. The noise matrix E is set as 0 in the noiseless case, while under the noisy setting, we generate each element of the noise matrix E from i.i.d. centered Gaussian distribution with variance ", " = 0.25. |