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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Structured BFGS Method for Optimal Doubly Stochastic Matrix Approximation
Authors: Dejun Chu, Changshui Zhang, Shiliang Sun, Qing Tao
AAAI 2023 | Venue PDF | 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 EMAIL, EMAIL, EMAIL, EMAIL |
| 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 ο¬rst two experiments with the same stopping tolerance, i.e., Fk Ο΅ = 10 12. ... We set Ο = 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. |