| Topic: | Fast Implementation of Galerkin eigenvectors and eigenvalues estimation with kernel methods developped in [PIL20], [CAB21], [PIL23], [CAB23]. |
|---|---|
| Author: | Vivien Cabannes |
| Nighly Version: | 0.0.3 |
| Stable Version: | 0.0.2 of 2023/03/21 |
You can download our package from its pypi repository.
$ pip install klapYou can download source code at https://github.com/VivienCabannes/laplacian/archive/master.zip. Once download, our packages can be install through the following command.
$ cd <path to code folder>
$ pip install -e .The -e option is notably useful to add kernel and modify the codebase.
See notebooks folder.
- Most of the code is based on the following python libraries:
- scipy
- numpy
- numba
- Testing done with notebook are based on:
- jupyter-notebook
- matplotlib
- pandas
The code could easily be rewritten for pytorch (with jit support). For generalized eigenvalues decomposition, see torch.lobpcg.
| [PIL20] | Statistical estimation of the poincaré constant and application to sampling multimodal distributions, Loucas Pillaud-Vivien, Francis Bach, Tony Lelièvre, Alessandro Rudi, Gabriel Stoltz, AISTATS, 2020. |
| [CAB21] | Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning, Vivien Cabannes, Loucas Pillaud-Vivien, Francis Bach and Alessandro Rudi, NeurIPS, 2021. |
| [PIL23] | Kernelized Diffusion maps, Loucas Pillaud-Vivien and Francis Bach, COLT, 2023. |
| [CAB23] | The Galerkin method beats Graph-Based Approaches for Spectral Algorithms, Vivien Cabannes and Francis Bach, AISTATS, 2024. |