In this paper, we introduce McTorch, a manifold optimization library for deep learning that extends PyTorch. It aims to lower the barrier for users wishing to use manifold constraints in deep learning applications, i.e., when the parameters are constrained to lie on a manifold. Such constraints include the popular orthogonality and rank constraints, and have been recently used in a number of applications in deep learning. McTorch follows PyTorch's architecture and decouples manifold definitions and optimizers, i.e., once a new manifold is added it can be used with any existing optimizer and vice-versa. McTorch is available at https://github.com/mctorch .
Cite
@article{arxiv.1810.01811,
title = {McTorch, a manifold optimization library for deep learning},
author = {Mayank Meghwanshi and Pratik Jawanpuria and Anoop Kunchukuttan and Hiroyuki Kasai and Bamdev Mishra},
journal= {arXiv preprint arXiv:1810.01811},
year = {2018}
}