Fundamental Benefit of Alternating Updates in Minimax Optimization
Authors: Jaewook Lee, Hanseul Cho, Chulhee Yun
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7. Experiments and subsequent subsections describe empirical evaluation. For instance, We conducted experiments on a (3 + 3)-dimensional SCSC quadratic game to visually compare the convergence speed of the algorithms in Figure 1. and We run further experiments on (100 + 100)-dimensional SCSC quadratic games to extensively compare GDA, EG, OGD, and Alex-GDA. |
| Researcher Affiliation | Academia | 1KAIST AI, South Korea. |
| Pseudocode | Yes | Algorithm 1 Sim-GDA and Alt-GDA and Algorithm 2 Alternating-Extrapolation GDA (Alex-GDA). |
| Open Source Code | Yes | The experiments in Tables 1 and 2 can be reproduced with our code available at Git Hub.3 github.com/Hanseul Jo/Alex-GDA |
| Open Datasets | Yes | For MNIST (Deng, 2012)... For CIFAR-10 (Krizhevsky et al., 2009)... For LSUN-Bedroom 64 64 dataset (Yu et al., 2015)... |
| Dataset Splits | No | The paper describes using MNIST, CIFAR-10, and LSUN Bedroom datasets but does not explicitly provide details about train/validation/test splits, only mentioning training for GANs. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory specifications used for experiments. |
| Software Dependencies | No | The paper mentions using PyTorch and Adam optimizer but does not specify their version numbers or other software dependencies with version information. |
| Experiment Setup | Yes | Parameter tuning. We tuned step sizes and other parameters (like γ and δ of Alex-GDA) by grid search... We tune the momentum parameters mx, my { 0.99, 0.95, 0.9, 0.8, 0.7, ..., 0.9, 0.95, 0.99}. We tune γ and δ for Alex-GDA as γ, δ {0.5, 0.6, 0.7, ..., 3.0}. |