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..
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization
Authors: Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael Jordan
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically study the performance of our AG-OG with restarting algorithm. In these experimental results, we study both deterministic [ B.1] and stochastic settings [ B.2], each of which we compare the state-of-the-art algorithms. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley 2Department of Computer Sciences, University of California, Los Angeles 3DIRO, Universit e de Montr eal and Mila 4Department of Statistics, University of California, Berkeley. |
| Pseudocode | Yes | Algorithm 1 Accelerated Gradient-Optimistic Gradient (AG-OG)(zag 0 , z0, z 1/2, K), Algorithm 2 Accelerated Gradient-Optimistic Gradient with restarting (AG-OG with restarting), Algorithm 3 Stochastic Accelerated Gradient-Optimistic Gradient (S-AG-OG)(zag 0 , z0, z 1/2, K) |
| Open Source Code | No | The paper does not provide explicit statements or links indicating the release of open-source code for the described methodology. |
| Open Datasets | No | We present results on synthetic quadratic game datasets: x A1x + y A2x y A3y, with various selections of the eigenvalues of A1, A2, A3. |
| Dataset Splits | No | The paper discusses convergence and empirical performance on synthetic datasets but does not describe train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | We use stepsize ηk = k+2 3LH(k+2) in both the AG-OG and the AG-OG with restarting algorithms and restart AG-OG with restarting once every 100 iterates. For the OGDA algorithm, we take stepsize η = 1 2(L LH) as is indicated by recent arts e.g. (Mokhtari et al., 2020b). |