McTorch, a manifold optimization library for deep learning

2018/10/03 by Mayank Meghwanshi, Pratik Jawanpuria, Meghwanshi, Mayank +7 · 3 citations
Computer Science · #Advanced Vision and Imaging #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Video Surveillance and Tracking Methods

paper · pdf · doi:10.48550/arxiv.1810.01811

openalex publication_date 2018/10/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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 .

Cited by

• A Retraction-free Method for Nonsmooth Minimax Optimization over a Compact Manifold
• SECA: Semantically Equivalent and Coherent Attacks for Eliciting LLM Hallucinations
• Riemannian adaptive stochastic gradient algorithms on matrix manifolds

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