Graded Symmetry Groups: Plane and Simple
Not on the map. Why?
Abstract (arXiv)
The symmetries described by Pin groups are the result of combining a finite number of discrete reflections in (hyper)planes. The current work shows how an analysis using geometric algebra provides a picture complementary to that of the classic matrix Lie algebra approach, while retaining information about the number of reflections in a given transformation. This imposes a graded structure on Lie groups, which is not evident in their matrix representation. By embracing this graded structure, the invariant decomposition theorem was proven: any composition of k linearly independent reflections can be decomposed into lceil k/2 rceil commuting factors, each of which is the product of at most two reflections. This generalizes a conjecture by M. Riesz, and has e.g. the Mozzi-Chasles' theorem as its 3D Euclidean special case. To demonstrate its utility, we briefly discuss various examples such as Lorentz transformations, Wigner rotations, and screw transformations. The invariant decomposition also directly leads to closed form formulas for the exponential and logarithmic function for all Spin groups, and identifies element of geometry such as planes, lines, points, as the invariants of k-reflections. We conclude by presenting novel matrix/vector representations for geometric algebras ℝpqr, and use this in E(3) to illustrate the relationship with the classic covariant, contravariant and adjoint representations for the transformation of points, planes and lines.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jul 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 4; 2024: 1; 2025: 4; 2026: 2.
11 papers.
- 2023Clifford Group Equivariant Neural Networkscited by 54
- 2023Geometric Algebra Transformercited by 46
- 2023Geometric Clifford Algebra Networkscited by 37
- 2024Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physicscited by 36
- 2023Machine Learning Clifford invariants of ADE Coxeter elementscited by 6
- 2025Hybrid Diffusion Policies with Projective Geometric Algebra for Efficient Robot Manipulation Learningcited by 3
- 2025Composing Linear Layers from Irreduciblescited by 1
- 2025Clifford Algebraic Rotor Embeddings : Maybe embeddings should start to CAREcited by 1
- 2025Soft Geometric Inductive Bias for Object Centric Dynamicscited by 0
- 2026On Lie Groups Preserving Subspaces of Degenerate Clifford Algebrascited by 0
- 2026A Geometric Algebra-informed NeRF Framework for Generalizable Wireless Channel Predictioncited by 0
- 2026A Geometric Algebra-informed NeRF Framework for Generalizable Wireless Channel Prediction13 Apr 2026
- 2026On Lie Groups Preserving Subspaces of Degenerate Clifford Algebras12 Jan 2026
- 2025Soft Geometric Inductive Bias for Object Centric Dynamics17 Dec 2025
- 2025Clifford Algebraic Rotor Embeddings : Maybe embeddings should start to CARE11 Nov 2025
- 2025Composing Linear Layers from Irreducibles15 Jul 2025
- 2025Hybrid Diffusion Policies with Projective Geometric Algebra for Efficient Robot Manipulation Learning8 Jul 2025
- 2024Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics23 May 2024
- 2023Machine Learning Clifford invariants of ADE Coxeter elements29 Sep 2023
- 2023Geometric Algebra Transformer28 May 2023
- 2023Clifford Group Equivariant Neural Networks18 May 2023
- 2023Geometric Clifford Algebra Networks13 Feb 2023