Solving Most Systems of Random Quadratic Equations
Authors: Gang Wang, Georgios Giannakis, Yousef Saad, Jie Chen
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical tests using both synthetic data and real images corroborate its improved signal recovery performance and computational efficiency relative to state-of-the-art approaches. |
| Researcher Affiliation | Academia | Key Lab of Intell. Contr. and Decision of Complex Syst., Beijing Inst. of Technology Digital Tech. Center & Dept. of Electrical and Computer Eng., Univ. of Minnesota Department of Computer Science and Engineering, Univ. of Minnesota {gangwang, georgios, saad}@umn.edu; chenjie@bit.edu.cn. |
| Pseudocode | Yes | Algorithm 1 Reweighted Amplitude Flow |
| Open Source Code | Yes | For reproducibility, the Matlab code of the RAF algorithm is publicly available at https://gangwg.github.io/RAF/. |
| Open Datasets | No | The paper uses 'synthetic data' where the true signal vector x was randomly generated using x N(0, I), and the i.i.d. sensing vectors ai ai N(0, I). It also mentions a 'Galaxy image' downloaded from 'http://pics-about-space.com/milky-way-galaxy.', but this is an image, not a structured public dataset with access details. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | All experiments were performed using MATLAB on an Intel CPU @ 3.4 GHz (32 GB RAM) computer. |
| Software Dependencies | No | The paper mentions 'MATLAB' as the software used but does not provide a specific version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | Algorithm 1: 'maximum number of iterations T; step size µt = 2/6 and weighting parameter βi = 10/5 for real/complex Gaussian model; |S| = 3m/13 , and γ = 0.5.' Section 2.3: 'we take |S| := 3m/13 , βi β := 10, γ := 0.5, and µ := 2.' Section 4: 'Each scheme obtained the initial guess based on 200 power iterations, followed by a series of T = 2, 000 (truncated/reweighted) gradient iterations.' |