Optimal Algorithms for Decentralized Stochastic Variational Inequalities
Authors: Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtarik, Alexander Gasnikov
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on bilinear problems and robust regression problems confirm the practical efficiency of our methods, both in the non-distributed stochastic setup and in the decentralized deterministic one. |
| Researcher Affiliation | Collaboration | Dmitry Kovalev KAUST , Saudi Arabia dakovalev1@gmail.com Aleksandr Beznosikov MIPT , HSE University and Yandex, Russia anbeznosikov@gmail.com Abdurakhmon Sadiev MIPT, Russia sadiev.aa@phystech.edu Michael Persiianov MIPT, Russia persiianov.mi@phystech.edu Peter Richtárik KAUST, Saudi Arabia peter.richtarik@kaust.edu.sa Alexander Gasnikov MIPT, HSE University and IITP RAS , Russia gasnikov@yandex.ru |
| Pseudocode | Yes | Algorithm 1 |
| Open Source Code | No | we run simple experiments just for theoretical purpose, it is easy to rerun it |
| Open Datasets | Yes | We take datasets from Li BSVM [19] and divided unevenly across M = 25 workers. For communication networks we chose the star, the ring and the grid topologies. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) is provided for train/validation/test sets. |
| Hardware Specification | No | we run simple experiments just for theoretical purpose, they require no computing power and can be run on any laptop |
| Software Dependencies | No | The paper mentions 'LibSVM' as a source for datasets but does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | The parameters of all methods are selected in two ways: 1) as described in the theory of the corresponding papers, and 2) tuned for the best convergence. We run all methods with different batch sizes. The comparison criterion is the number of epochs (one full gradient = epoch). |