Pith. sign in

REVIEW 2 cited by

Introduction to Clifford's Geometric Algebra

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1306.1660 v1 pith:RQRLU2RC submitted 2013-06-07 math.RA

Eckhard Hitzer This is my paper · ORCID
classification math.RA
keywords geometricalgebracliffordgroupshypercomplexprocessingalgebrasalgorithms
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Geometric algebra was initiated by W.K. Clifford over 130 years ago. It unifies all branches of physics, and has found rich applications in robotics, signal processing, ray tracing, virtual reality, computer vision, vector field processing, tracking, geographic information systems and neural computing. This tutorial explains the basics of geometric algebra, with concrete examples of the plane, of 3D space, of spacetime, and the popular conformal model. Geometric algebras are ideal to represent geometric transformations in the general framework of Clifford groups (also called versor or Lipschitz groups). Geometric (algebra based) calculus allows, e.g., to optimize learning algorithms of Clifford neurons, etc. Keywords: Hypercomplex algebra, hypercomplex analysis, geometry, science, engineering.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Agnostic Approach to Building Empirical Type Ia Supernova Light Curves: Evidence for Intrinsic Chromatic Flux Variation Using Nearby Supernova Factory Data

    astro-ph.HE 2025-05 conditional novelty 7.0 of 10

    A new two-template empirical SN Ia color model trained on Nearby Supernova Factory data finds that about 13% of phase-independent flux variation is not explained by dust.

  2. Composing Linear Layers from Irreducibles

    cs.LG 2025-07 reject novelty 6.0 of 10

    A rotor-based layer built from bivector exponentials approximates LLM attention projections with O(log^2 d) parameters and competitive downstream performance.

Pith tools