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PreprintCC BY 4.0arXiv

Bi-invariant Geodesic Regression: Existence, Uniqueness, and Convergence

  • Martin Hanik
  • Christoph von Tycowicz
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Abstract

Bi-invariant geodesic regression generalizes linear regression to Lie groups. Its main feature is that it respects the symmetries of the group so that the resulting estimator is independent of arbitrary choices such as a reference frame. However, the local existence and uniqueness of the underlying estimator have not been shown until now. Furthermore, the convergence properties of the proposed algorithm for computing the estimator are not known. In this work, we investigate these questions. We prove that, locally, a unique estimator exists and give explicit bounds on the size of this neighborhood. We also show that the proposed iterative algorithm converges linearly to this estimator.

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Cite

BibTeXRIS
BibTeX
@misc{hanik2026biinvariant,
  title = {{Bi-invariant Geodesic Regression: Existence, Uniqueness, and Convergence}},
  author = {Martin Hanik and Christoph von Tycowicz},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.31526},
  eprint = {2609.31526},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.31526},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Bi-invariant Geodesic Regression: Existence, Uniqueness, and Convergence
AU  - Martin Hanik
AU  - Christoph von Tycowicz
PY  - 2026
DA  - 2026/09/25
PB  - arXiv
DO  - 10.48550/arxiv.2609.31526
UR  - https://arxiv.org/abs/2609.31526
AB  - Bi-invariant geodesic regression generalizes linear regression to Lie groups. Its main feature is that it respects the symmetries of the group so that the resulting estimator is independent of arbitrary choices such as a reference frame. However, the local existence and uniqueness of the underlying estimator have not been shown until now. Furthermore, the convergence properties of the proposed algorithm for computing the estimator are not known. In this work, we investigate these questions. We prove that, locally, a unique estimator exists and give explicit bounds on the size of this neighborhood. We also show that the proposed iterative algorithm converges linearly to this estimator.
ER  -