Accelerated Single-Call Methods for Constrained Min-Max Optimization
Authors: Yang Cai, Weiqiang Zheng
ICLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also provide illustrative numerical experiments in Appendix E. |
| Researcher Affiliation | Academia | Yang Cai Yale University EMAIL Weiqiang Zheng Yale University EMAIL |
| Pseudocode | No | The paper describes update rules for algorithms (e.g., Optimistic Gradient (OG) and Accelerated Reflected Gradient (ARG)) in text, but it does not present them in a formalized pseudocode block or algorithm environment. |
| Open Source Code | Yes | The code can be found in the Supplementary Material. |
| Open Datasets | No | The paper describes a 'Test Problem' used for numerical experiments (Problem 1 in (Malitsky, 2015)) which is a specific mathematical formulation, not a public dataset in the traditional sense of a collection of data samples for training. No explicit access information (link, DOI, citation with authors/year) is provided for a public dataset. |
| Dataset Splits | No | The paper describes a 'Test Problem' and experimental setup, but it does not mention any training, validation, or testing splits for a dataset. The evaluation is based on a convergence criterion (residual). |
| Hardware Specification | Yes | We run experiments using Python 3.9 on jupyter-notebook, on Mac Book Air (M1, 2020) running mac OS 12.5.1. |
| Software Dependencies | Yes | We run experiments using Python 3.9 on jupyter-notebook, on Mac Book Air (M1, 2020) running mac OS 12.5.1. |
| Experiment Setup | Yes | We denote η to be the step size and the termination criteria is the residual (operator norm) ||F(zt)|| ε. ... With step size η = 0.4, EG is slower than RG. ... With the optimized step size η = 0.7... With step size η = 0.5, FEG is slower than ARG. With the optimized step size η = 1... |