The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization
Authors: Dmitry Kovalev, Alexander Gasnikov
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theoretical work with no forseeable negative societal impact. |
| Researcher Affiliation | Academia | Dmitry Kovalev KAUST dakovalev1@gmail.com King Abdullah University of Science and Technology, Thuwal, Saudi Arabia Alexander Gasnikov MIPT , ISP RAS , HSE gasnikov@yandex.ru Moscow Institute of Physics and Technology, Dolgoprudny, Russia Institute for System Programming RAS, Research Center for Trusted Artificial Intelligence, Moscow, Russia National Research University Higher School of Economics, Moscow, Russia |
| Pseudocode | Yes | Algorithm 1 Accelerated Gradient Method; Algorithm 2 Accelerated Proximal Point Algorithm; Algorithm 3 Extra Anchored Gradient for Monotone Inclusions; Algorithm 4 FOAM: The First Optimal Algorithm for Minimax Optimization |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not involve training on datasets. It states 'N/A' for questions related to experiments and data. |
| Dataset Splits | No | The paper is theoretical and does not involve data splits for training, validation, or testing. It states 'N/A' for questions related to experiments and data. |
| Hardware Specification | No | The paper is theoretical and does not describe hardware used for experiments. It states 'N/A' for questions related to running experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for experimental reproducibility. It states 'N/A' for questions related to running experiments. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, such as hyperparameters or system-level training settings. It states 'N/A' for questions related to running experiments. |