Riemannian Diffusion Adaptation for Distributed Optimization on Manifolds
Authors: Xiuheng Wang, Ricardo Augusto Borsoi, Cédric Richard, Ali H. Sayed
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the algorithm to the online decentralized principal component analysis problem and Gaussian mixture model inference. Experimental results with both synthetic and real data illustrate its performance. |
| Researcher Affiliation | Academia | 1Universit e de Lorraine, CNRS, CRAN, France 2Universit e Cˆote d Azur, CNRS, OCA, France 3 Ecole Polytechnique F ed erale de Lausanne, Switzerland. |
| Pseudocode | Yes | Algorithm 1 Riemannian Diffusion Adaptation |
| Open Source Code | Yes | Opensource code to reproduce the results is publicly available on https://github.com/xiuheng-wang/diffusion_manifold_release. |
| Open Datasets | Yes | Real data: We also obtain numerical results on the MNIST dataset (Le Cun, 1998). |
| Dataset Splits | Yes | We randomly shuffle the images, partition them into K = 20 subsets, and then run the algorithms to compute the first p = 5 principal components with the fixed step sizes µ = 0.002 and α = 0.005. |
| Hardware Specification | Yes | These experiments were performed on a computer with an Apple M4 Pro processor and 24GB of RAM. |
| Software Dependencies | No | Our method is implemented in Python with the Pymanopt toolbox (Townsend et al., 2016). |
| Experiment Setup | Yes | The simulations used fixed step sizes µ = 0.05 and α = 0.8. |