Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique
Authors: Taeho Yoon, Jaeyeon Kim, Jaewook J. Suh, Ernest K. Ryu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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. |