Inexact trust-region algorithms on Riemannian manifolds
Authors: Hiroyuki Kasai, Bamdev Mishra
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical evaluations demonstrate that the proposed algorithms outperform state-of-the-art Riemannian deterministic and stochastic gradient algorithms across different applications. |
| Researcher Affiliation | Collaboration | Hiroyuki Kasai The University of Electro-Communications Japan kasai@is.uec.ac.jp Bamdev Mishra India bamdevm@microsoft.com |
| Pseudocode | Yes | Algorithm 1 Inexact Riemannian trust-region (Inexact RTR) algorithm |
| Open Source Code | Yes | The implementation of the proposed algorithms uses the MATLAB toolbox Manopt [40] and is available at https://github.com/hiroyuki-kasai/Subsampled-RTR. |
| Open Datasets | Yes | We use three real-world datasets: Yale B [46], COIL-100 [47], and CIFAR-100 [48]... two real-world datasets with r = 10, where Case P3 deals with the MNIST dataset [52]... deals with the Covertype dataset [53]... Jester dataset 1 [54] |
| Dataset Splits | No | The paper does not explicitly mention training/validation/test dataset splits, percentages, or absolute counts for validation. |
| Hardware Specification | Yes | All simulations are performed in MATLAB on a 4.0 GHz Intel Core i7 machine with 32 GB RAM. |
| Software Dependencies | No | The paper mentions 'MATLAB toolbox Manopt' but does not provide specific version numbers for either MATLAB or Manopt. |
| Experiment Setup | Yes | By following [38], we set |Sg| = n/10 and |SH| = n/102 except Cases P5, P6, M4, and M5. We set the batch-size to n/10 in RSVRG. All simulations are performed in MATLAB on a 4.0 GHz Intel Core i7 machine with 32 GB RAM. |