Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization
Authors: Haochuan Li, Yi Tian, Jingzhao Zhang, Ali Jadbabaie
NeurIPS 2021 | Conference 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 haochuan@mit.edu Yi Tian Department of EECS MIT Cambridge, MA 02139 yitian@mit.edu Jingzhao Zhang Department of EECS MIT Cambridge, MA 02139 jzhzhang@mit.edu Ali Jadbabaie Department of CEE MIT Cambridge, MA 02139 jadbabai@mit.edu |
| 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. |