# Automatic Differentiation for Complex Valued SVD

arXiv: https://arxiv.org/abs/1909.02659 · HTML: https://mlsanity.com/paper/1909.02659/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Scalably learning quantum many-body Hamiltonians from dynamical data](https://mlsanity.com/paper/2209.14328/) (2022)
- [Optimal Matrix-Mimetic Tensor Algebras via Variable Projection](https://mlsanity.com/paper/2406.06942/) (2024)
- [Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications](https://mlsanity.com/paper/2108.13831/) (2021)

## Abstract (arXiv)

In this note, we report the back propagation formula for complex valued singular value decompositions (SVD). This formula is an important ingredient for a complete automatic differentiation(AD) infrastructure in terms of complex numbers, and it is also the key to understand and utilize AD in tensor networks.