PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
December 8, 2025Archive for History of Exact Sciences3 citationsOpen Access

Galois’s lost insight: the overlooked brilliance of his last publication

View Full Paper
LJLizhen JiLJLizhen Ji

Key Points

  • This work evaluates Galois's last publication for its mathematical and historical significance.
  • Reexamine Galois's last publication
  • Compare with his other minor manuscripts
  • Assess Neumann's critiques
  • Identified flaws in Galois's proof of differentiability
  • Highlighted Galois's novel method for deriving curvature
  • Challenged misconceptions of Galois's contributions

Abstract

Abstract Évariste Galois is celebrated for his groundbreaking contributions to algebra and group theory, but his last published paper, containing two essays, has been largely ignored by historians. We will show that the first essay contains an incorrect proof of the statement that continuous functions are differentiable, due to several very naive mistakes. We will see that the second essay contains an elegant and novel derivation of the formula for the curvature of space curves. While the formula itself was already known to Euler and Cauchy, Galois’s method, which uses a family of planes, is strikingly original and conceptually insightful. This paper reevaluates both the historical and mathematical significance of Galois’s last publication, comparing it with his other minor manuscripts and challenging Neumann’s dismissal of its value in his edition of Galois’s works (Neumann 2011). By highlighting Galois’s overlooked contribution to differential geometry, this paper provides a fuller picture of his mathematical genius, thereby helping to avoid the Whiggish tendencies found in many studies of his work.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Ji et al. (2025) studied this question.

synapsesocial.com/papers/693624c34fa91c937236cd25https://doi.org/10.1007/s00407-025-00355-7
Ask AI
Helpful
Bookmark
Share
View Full Paper