#eigenvalue
Live, measured metrics for the hashtag #eigenvalue from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #eigenvalue
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Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-09-12 06:40 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-09-12 06:40 UTCLive pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-09-12 06:40 UTCEverything below is measured over the latest 10 public posts (spanning ~49341 hours).
Posting hours (UTC)
Languages: English (9) · Chinese (1)
Avg boosts / post: 0.3
Top of the latest posts
via MathMatize@X #GaussianCurvature #EigenValue #Isometry #Invariance #IsometryInvariance #Pizza #PizzaMath #Maths #Math #MathMeme #MathsMeme #MathMemes #MathsMemes #Mathematics #Mathematicians #Mathematician
... that's nothing new. the point was to address a related question: suppose that the eigensystem {v_i, λ_i}, i = 1, ..., n of a full-rank, well-conditioned n-by-n square matrix A is known, and then you are given a related matrix B = A + E,
Thanks to the Manchester NA group for organizing a seminar by David Watkins, one of the foremost experts on matrix eigenvalue algorithms. I find numerical linear algebra talks often too technical, but I could follow David's talk quite well
What “eigenvalue” means
Wiktionary · Wikipediaeigenvalue/ˈaɪɡənˌvæljuː/
- nounA scalar, \lambda, such that there exists a non-zero vector x (a corresponding eigenvector) for which the image of x under a given linear operator \mathrm{A} is equal to the image of x under multiplication by \lambda; i.e. \mathrm{A} x = \lambda x.
In linear algebra, an eigenvector or characteristic vector is a (nonzero) vector that has its direction unchanged by a given linear transformation. More precisely, an eigenvector of a linear transformation is scaled by a constant factor when the linear transformation is applied to it: . The corresponding eigenvalue, characteristic value, or characteristic root is the multiplying factor .
“Eigenvalues and eigenvectors” on Wikipedia (CC BY-SA) →#eigenvalue across platforms
every network with a public tag surfaceFollow #eigenvalue straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/eigenvalue