Structured BFGS Method for Optimal Doubly Stochastic Matrix Approximation
Authors: Dejun Chu, Changshui Zhang, Shiliang Sun, Qing Tao
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify the advantages of our approach on both synthetic data and real data sets. The experimental results demonstrate that our algorithm outperforms the state-of-the-art solvers and enjoys outstanding scalability. |
| Researcher Affiliation | Academia | Dejun Chu1, Changshui Zhang2, Shiliang Sun3, Qing Tao4 1 School of Software, Hefei University of Technology 2 Department of Automation, Tsinghua University 3 School of Computer Science and Technology, East China Normal University 4 Army Academy of Artillery and Air Defense djun.chu@gmail.com, zcs@mail.tsinghua.edu.cn, slsun@cs.ecnu.edu.cn, taoqing@gmail.com |
| Pseudocode | Yes | Algorithm 1: Structured BFGS Algorithm for the Dual Problem (2) and Algorithm 2: Newton-based Line Search Method for Solving the Sub-problem (12) |
| Open Source Code | Yes | Our code will be released publicly on the github1 for reproducing all the results of this section. 1https://github.com/djchu/sbfgs4dsm |
| Open Datasets | Yes | We test 6 instances of the given matrices A which are derived from the real LIBSVM data sets at https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/. |
| Dataset Splits | No | The paper uses LIBSVM datasets and synthetic data, but does not explicitly describe specific train/validation/test dataset splits needed for reproduction. |
| Hardware Specification | Yes | All the algorithms have been implemented in MATLAB R2019b and run on a 3.00-GHz Intel Core i9 Linux machine with 128GB memory. |
| Software Dependencies | Yes | All the algorithms have been implemented in MATLAB R2019b |
| Experiment Setup | Yes | For the fairness of experimental comparison, we terminate three algorithms in the first two experiments with the same stopping tolerance, i.e., Fk ϵ = 10 12. ... We set σ = 1.0 in this subsection. ... We follow the setting of alternating projection algorithm (Zass and Shashua 2006) on six real data sets listed in Table 1. |