Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization
Authors: Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael Jordan
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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). |