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..
Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique
Authors: Taeho Yoon, Jaeyeon Kim, Jaewook J. Suh, Ernest K. Ryu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 8, the paper presents 'numerical simulations illustrating the dynamics of dual-anchor algorithm.' It includes 'Figure 1. Trajectories generated by minimax optimization algorithms' and 'Figure 2. Performance of minimax algorithms in reducing L(xk) 2 for bilinear problem instances.', indicating empirical evaluation. |
| Researcher Affiliation | Academia | The authors' affiliations are listed as: '1Department of Mathematical Sciences, Seoul National University' and '2Department of Mathematics, University of California, Los Angeles.' Both are academic institutions. |
| Pseudocode | No | The paper presents algorithms like OHM, Dual-OHM, FEG, and Dual-FEG as mathematical update rules within the main text. It does not include dedicated pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not include any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | Yes | The paper refers to a 'worst-case bilinear example due to Ouyang & Xu (2021)' in Section 8 and provides detailed construction in Appendix I.2, citing 'Ouyang, Y. and Xu, Y. Lower complexity bounds of first-order methods for convex-concave bilinear saddle-point problems. Mathematical Programming, 185(1):1–35, 2021.' This constitutes access to an open dataset via citation. |
| Dataset Splits | No | The paper describes experiments on specific problem instances and synthetic data, e.g., 'L(u, v) = uv' or 'L(u, v) = u^2v'. It mentions 'initial points u0 = 0, v0 = 0' or 'u0, v0 with i.i.d. standard normal coordinates'. However, it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages or libraries. |
| Experiment Setup | Yes | Section 8, 'Experiments', provides specific parameter values for the algorithms: 'α = 0.005 and N = 5000', 'α = 0.05 and N = 10000', 'α = 1.0 and N = 10000', 'µ = 0.1', and 'δ = 0.1, α = 0.5 and N = 10^5'. These are explicit hyperparameters. |