Stochastic Subspace Cubic Newton Method
Authors: Filip Hanzely, Nikita Doikov, Yurii Nesterov, Peter Richtarik
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments show that SSCN outperforms non-accelerated first-order CD algorithms while being competitive to their accelerated variants. 8. Experiments We now numerically verify our theoretical claims. |
| Researcher Affiliation | Academia | 1King Abdullah University of Science and Technology, Thuwal, Saudi Arabia 2Catholic University of Louvain, Louvain-la-Neuve, Belgium. |
| Pseudocode | Yes | Algorithm 1 SSCN: Stochastic Subspace Cubic Newton 1: Initialization: x0, distribution D of random matrices with d rows and full column rank 2: for k = 0, 1, . . . do 3: Sample S from distribution D 4: hk = argminh Rτ(S) TS(xk, h) 5: Set xk+1 = xk + Shk |
| Open Source Code | No | The paper does not provide any explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We consider binary classification with LIBSVM (Chang & Lin, 2011) data modelled by regularized logistic regression. |
| Dataset Splits | No | The paper mentions using LIBSVM data but does not explicitly provide training, validation, or test dataset split percentages or counts in the main text provided. |
| Hardware Specification | No | The paper discusses computational cost in terms of operations (e.g., O(n), O(nτ^2 + τ^3)) but does not provide specific hardware details such as GPU/CPU models, memory, or processing units used for experiments. |
| Software Dependencies | No | The paper mentions 'LIBSVM' as data source and references various methods and frameworks (e.g., FGM) but does not list specific software dependencies with version numbers used for implementing their method or running experiments. |
| Experiment Setup | No | The paper states 'the exact setup for this experiment can be found in Section B of the Appendix' but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) in the main text. |