semvec/pypi-packages huggingface dataset:
torch-householder 1.0.1
Efficient Householder Transformation in PyTorch
License: BSD
Requires Python: >=3.6
Keywords: pytorch,householder,orgqr,householer_product,efficient,differentiable,orthogonal,transformation,unitary,matrices,stiefel,manifold


This package implements the Householder transformation algorithm for calculating orthogonal matrices and orthonormal 
frames with differentiable bindings to PyTorch. In particular, the package provides an enhanced drop-in replacement for 
the `torch.orgqr` function, which was renamed into `torch.linalg.householder_product` as of PyTorch 1.9. 

APIs for orthogonal transformations have been around since LAPACK; however, their support in the deep learning 
frameworks is lacking. Recently, orthogonal constraints have become popular in deep learning as a way to regularize
models and improve training dynamics, and hence the need to backpropagate through orthogonal transformations arised.

PyTorch 1.7 implements matrix exponential function `torch.matrix_exp`, which can be repurposed to performing the 
orthogonal transformation when the input matrix is skew-symmetric. This is the baseline we use in Speed and Precision 
evaluation.   

PyTorch 1.9 renamed `torch.orgqr` into `torch.linalg.householder_product`, and added support of autograd, batching, and 
GPU execution.

Compared to `torch.matrix_exp`, the Householder transformation implemented in this package has the following advantages: 
- Orders of magnitude lower memory footprint
- Ability to transform non-square matrices (orthonormal frames)
- A significant speed-up for non-square matrices
- Better numerical precision for all matrix and batch sizes

Find more details and the most up-to-date information on the project webpage:
https://www.github.com/toshas/torch-householder