Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization
Authors: Haochuan Li, Yi Tian, Jingzhao Zhang, Ali Jadbabaie
NeurIPS 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work is of theoretical nature. The main contribution is to provide theoretical complexity lower bounds. |
| Researcher Affiliation | Academia | Haochuan Li Department of EECS MIT Cambridge, MA 02139 EMAIL Yi Tian Department of EECS MIT Cambridge, MA 02139 EMAIL Jingzhao Zhang Department of EECS MIT Cambridge, MA 02139 EMAIL Ali Jadbabaie Department of CEE MIT Cambridge, MA 02139 EMAIL |
| Pseudocode | No | The paper focuses on theoretical derivations and constructions, and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code. The ethics statement indicates N/A for code reproduction. |
| Open Datasets | No | This paper is theoretical and does not involve datasets, training, or empirical evaluation. |
| Dataset Splits | No | This paper is theoretical and does not involve datasets or data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any software dependencies or versions for experimental reproduction. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training details. |