Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization
Authors: Dmitry Kovalev, Alexander Gasnikov
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,926 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,108 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we study the fundamental open question of finding the optimal highorder algorithm for solving smooth convex minimization problems. ... We fix this fundamental issue by providing the first algorithm with O ϵ 2/(3p+1) p-th order oracle complexity. ... In this paper we provide an experimental comparison of the proposed optimal high-order algorithm for solving smooth convex minimization problems (Algorithm 4) with the existing near-optimal high-order method of Gasnikov et al. (2019a); Bubeck et al. (2019); Jiang et al. (2019) (Algorithm 2). In summary, the experiments show that the proposed optimal Algorithm 4 is indeed a practical algorithm which significantly outperforms the existing near-optimal Algorithm 2. The details can be found in Appendix F. |
| Researcher Affiliation | Academia | Dmitry Kovalev KAUST EMAIL Alexander Gasnikov MIPT , IITP RAS , HSE EMAIL King Abdullah University of Science and Technology, Thuwal, Saudi Arabia Moscow Institute of Physics and Technology, Dolgoprudny, Russia Institute for Information Transmission Problems RAS, Moscow, Russia National Research University Higher School of Economics, Moscow, Russia |
| Pseudocode | Yes | Algorithm 1 A-HPE Framework, Algorithm 2 Near-Optimal Tensor Method, Algorithm 3 Tensor Extragradient Method, Algorithm 4 Optimal Tensor Method |
| Open Source Code | No | N/A |
| Open Datasets | No | N/A. The paper mentions an 'experimental comparison' in Section 4.5 and Appendix F, but no specific dataset names with access information (citation, link, or repository) are provided in the main text or ethics statement. |
| Dataset Splits | No | N/A. The paper mentions an 'experimental comparison' in Section 4.5 and Appendix F, but no specific training, validation, or test splits are detailed in the main text or ethics statement. |
| Hardware Specification | No | The paper does not provide any specific hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for its experiments. |
| Experiment Setup | No | N/A. The paper mentions an 'experimental comparison' in Section 4.5 and Appendix F, but no specific experimental setup details, such as hyperparameters or training configurations, are provided in the main text or ethics statement. |