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..
Improved Algorithms for Convex-Concave Minimax Optimization
Authors: Yuanhao Wang, Jian Li
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies minimax optimization problems minx maxy f(x, y), where f(x, y) is mx-strongly convex with respect to x, my-strongly concave with respect to y and (Lx, Lxy, Ly)-smooth. Zhang et al. [42] provided the following lower bound of the gradient complexity for any ο¬rst-order method: Lx mx + L2xy mxmy + Ly my ln(1/Ο΅) . This paper proposes a new algorithm with gradient complexity upper bound O q Lx mx + L Lxy my ln (1/Ο΅) , where L = max{Lx, Lxy, Ly}. |
| Researcher Affiliation | Academia | Yuanhao Wang Computer Science Department Princeton University EMAIL Jian Li Institute for Interdisciplinary Information Sciences Tsinghua University EMAIL |
| Pseudocode | Yes | Algorithm 1 Alternating Best Response (ABR), Algorithm 2 Accelerated Proximal Point Algorithm for Minimax Optimization, Algorithm 3 APPA-ABR, Algorithm 4 Proximal Best Response, Algorithm 5 RHSS(k) (Recursive Hermitian-skew-Hermitian Split) |
| Open Source Code | No | The paper does not provide any statement or link for open-source code. |
| Open Datasets | No | The paper is theoretical and does not mention using or providing access information for a public dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not provide information about dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details like hyperparameters or training configurations for empirical evaluation. |