A Note on Sparse Generalized Eigenvalue Problem
Authors: Yunfeng Cai, Guanhua Fang, Ping Li
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments corroborate our theoretical findings via using alternating direction method of multipliers (ADMM)-based computational method. |
| Researcher Affiliation | Industry | Yunfeng Cai, Guanhua Fang, Ping Li Cognitive Computing Lab Baidu Research No. 10 Xibeiwang East Road, Beijing 100193, China 10900 NE 8th St. Bellevue, Washington 98004, USA {yunfengcai, guanhuafang, liping11}@baidu.com |
| Pseudocode | Yes | For computational purpose, we consider to solve (P1 ) by using the alternating direction method of multipliers (ADMM, [37, 21]). Specifically, we relax the problem (P1 ) by reformulating it to min x,y,z L(x, z, y), s.t. x T e Bx = 1, x 0 sn, (14) ... By formulation (14), we can optimize the objective function with respect to each variable iteratively. The steps for updating x, y, z are described as follows. i Update x: ... ii Update z: ... iii Update y: ... We call the above procedure as non convex-SGEP (NC-SGEP) algorithm. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. It describes generating synthetic data: "We sample n data which follows N(0, A) and sample another n data which follows N(0, B)." |
| 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 | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | Each setting is repeated for 100 times with fixed choice of λ = 0.3, η = 1, sn = 25. ... we let dimension p grow from 16 to 256, fix the sample size n = 100 and set λ = 0.5, η = 1 and sn = 50. |