A Nonconvex Optimization Framework for Low Rank Matrix Estimation
Authors: Tuo Zhao, Zhaoran Wang, Han Liu
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical experiments for matrix sensing to support our theoretical analysis. We choose m = 30, n = 40, and k = 5, and vary d from 300 to 900. Each entry of Ai s are independent sampled from N(0, 1). We then generate M = UV >, where e U 2 Rm k and e V 2 Rn k are two matrices with all their entries independently sampled from N(0, 1/k). We then generate d measurements by bi = h Ai, Mi for i = 1, ..., d. Figure 1 illustrates the empirical performance of the alternating exact minimization and alternating gradient descent algorithms for a single realization. |
| Researcher Affiliation | Academia | Tuo Zhao Johns Hopkins University Zhaoran Wang Han Liu Princeton University |
| Pseudocode | Yes | Algorithm 1 A family of nonconvex optimization algorithms for matrix sensing. |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes generating synthetic data for experiments ('Each entry of Ai s are independent sampled from N(0, 1). We then generate M = UV >'), but it does not use or provide access to a publicly available or open dataset. |
| Dataset Splits | No | The paper describes generating data for its experiments but does not specify any training, validation, or test dataset splits, percentages, or a methodology for creating such splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, libraries, or solvers with version numbers. |
| Experiment Setup | Yes | The step size for the alternating gradient descent algorithm is determined by the backtracking line search procedure. |