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 [1].
A Nonconvex Relaxation Approach for Rank Minimization Problems
Authors: Xiaowei Zhong, Linli Xu, Yitan Li, Zhiyuan Liu, Enhong Chen
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove theoretically that under certain assumptions our method achieves a high-quality local optimal solution efficiently. Experimental results on synthetic and real data show that the proposed ISTRA algorithm outperforms state-of-the-art methods in both accuracy and efficiency. |
| Researcher Affiliation | Academia | School of Computer Science and Technology University of Science and Technology of China, Hefei, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Iterative Shrinkage-Thresholding and Reweighted Algorithm (ISTRA) |
| Open Source Code | No | No statement or link indicates that the source code for the proposed methodology is publicly available. |
| Open Datasets | No | The paper uses synthetic data (generated as described) and real image data (shown in Figure 3) but does not provide access information (link, DOI, specific citation with authors/year, or mention of established benchmark dataset names with sources) for public availability. |
| Dataset Splits | No | The paper describes how data is generated and masked (e.g., 'observed entries', 'random mask'), but it does not provide specific percentages or counts for training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software or libraries used in the experiments. |
| Experiment Setup | No | While Algorithm 1 lists parameters like tmin, tmax, τ, r, λ, δ, ϵ, ρ, the paper does not specify the concrete numerical values used for these hyperparameters or other training configurations in the experiments. |