mlsanityData to 7 Oct 2026

Automatic Differentiation for Complex Valued SVD

arXiv 1909.02659 ↗First posted 4 Sep 2019math.NA cond-mat.stat-mech cond-mat.str-elcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Sep 2019.

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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.

3 papers.

  1. 2022Scalably learning quantum many-body Hamiltonians from dynamical datacited by 7
  2. 2024Optimal Matrix-Mimetic Tensor Algebras via Variable Projectioncited by 2
  3. 2021Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communicationscited by 0