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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Globally Q-linear Gauss-Newton Method for Overparameterized Non-convex Matrix Sensing
Authors: Xixi Jia, Fangchen FENG, Deyu Meng, Defeng Sun
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 32,616 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,516 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct experiments to demonstrate the effectiveness of the proposed AGN method for solving the over-parameterized non-convex matrix sensing problem. We set the ground truth matrix M = U ΣV , with U Rn r, V Rn r random orthogonal matrices and Σ is a diagonal matrix with condition number κ. We set n = 100, r = 5, d = 3r and the number of sensing matrices m = 50nr. All experiments were conducted using MATLAB on a Mac Book Pro with a 2.4 GHz quadcore Intel Core i5 CPU and 8 GB of memory. |
| Researcher Affiliation | Academia | Xixi Jia1 Fangchen Feng2 Deyu Meng3,4 Defeng Sun5 1School of Mathematics and Statistics, Xidian University 2L2TI Laboratory, University Sorbonne Paris Nord 3 School of Mathematics and Statistics, Xi an Jiaotong University 4 Macao Institute of Systems Engineering, Macau University of Science and Technology 5 Department of Applied Mathematics, The Hong Kong Polytechnic University |
| Pseudocode | Yes | A.1.2 The main AGN algorithm Algorithm 1: AGN for matrix sensing Data: A( ), b, η and the random Gauss initialization X0, t = 0. Result: The estimated solution X and the low-rank matrix ˆ M = PXX Q. while not end do t = t + 1; Update the approximated Gauss-Newton direction by Eq. (10); Update Xt and Xt+ 1 2 by Eq. (11); end |
| Open Source Code | Yes | The code for this paper is available at https://github.com/hsijiaxidian/AGN. |
| Open Datasets | No | We set the ground truth matrix M = U ΣV , with U Rn r, V Rn r random orthogonal matrices and Σ is a diagonal matrix with condition number κ. We set n = 100, r = 5, d = 3r and the number of sensing matrices m = 50nr. |
| Dataset Splits | No | The paper describes the generation of synthetic data and plots 'training curves' but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | Yes | All experiments were conducted using MATLAB on a Mac Book Pro with a 2.4 GHz quadcore Intel Core i5 CPU and 8 GB of memory. |
| Software Dependencies | No | All experiments were conducted using MATLAB... (no specific version of MATLAB or other software libraries with version numbers are mentioned). |
| Experiment Setup | Yes | We set the ground truth matrix M = U ΣV , with U Rn r, V Rn r random orthogonal matrices and Σ is a diagonal matrix with condition number κ. We set n = 100, r = 5, d = 3r and the number of sensing matrices m = 50nr. All the competing methods are initialized with random Gaussian matrix with zero mean the variance 1/n. |