Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery
Authors: Lijun Ding, Liwei Jiang, Yudong Chen, Qing Qu, Zhihui Zhu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The entire paper is a theoretical work, presenting definitions, theorems, proofs, and an algorithm (Algorithm 1) for a mathematical problem. There are no sections discussing empirical studies, datasets, performance metrics, or experimental results. For example, "Theorem 3.1", "Definition 2.1", "Algorithm 1" are prevalent throughout the document. |
| Researcher Affiliation | Academia | Xiaowei Zhang, Hongwei Liu, Guohua Liu, Lei Yang, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China, 100876. |
| Pseudocode | Yes | Algorithm 1: Subgradient Method for R-ORMR |
| Open Source Code | No | The paper does not provide any statement or link regarding the public release of source code for the described methodology. |
| Open Datasets | No | This theoretical paper does not involve experiments with datasets, and thus provides no information regarding publicly available or open datasets. |
| Dataset Splits | No | This theoretical paper does not involve experiments with datasets, and thus provides no information regarding dataset splits. |
| Hardware Specification | No | The paper does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | The paper, being purely theoretical, does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is a theoretical work and does not detail an experimental setup with hyperparameters or system-level training settings. |