Generator

detection-onset

One function in the curvature library, called 3 times across 1 essay. Below: what it draws at its defaults, what it draws at every value an essay asks for, the 12 claims it put to the test while drawing them, and where it stands against the rule this site is named for.

At its defaults it draws matrix–vector products before the curvature test fires, against the size of the negative eigenvalue, n = 40. A 40×40 matrix whose spectrum is positive over one decade apart from a single eigenvalue at −λ. At λ = 3 conjugate gradients meets a non-positive curvature after 3 products; at λ = 0.001 it takes 9. The trend is monotone and it runs the reassuring way: a Krylov space finds large eigenvalues first, so the indefiniteness that takes longest to detect is the indefiniteness that matters least. The direction that fires the test recovers between 12 and 42 per cent of λ, so it is a certificate rather than an estimate of the eigenvalue.

detection-onset is one function in lib/figures/curvature.js — negative curvature — the division that cannot be done, as an output. Everything below came out of it during this build, at arguments taken from the essays rather than invented for this page. A figure here is the figure a reader meets in an essay, and if the generator changes, this page changes with it.

At its defaults

Drawn even though every essay passes arguments — which on this site is every essay, at 100% of placements since the standard pass. A default nothing exercises is a trap for the next essay to call this with none, and this is the page where a default that has drifted from the figures around it becomes visible.

Matrix–vector products before the curvature test fires, against the size of the negative eigenvalue, n = 40A 40×40 matrix whose spectrum is positive over one decade apart from a single eigenvalue at −λ. At λ = 3 conjugate gradients meets a non-positive curvature after 3 products; at λ = 0.001 it takes 9. The trend is monotone and it runs the reassuring way: a Krylov space finds large eigenvalues first, so the indefiniteness that takes longest to detect is the indefiniteness that matters least. The direction that fires the test recovers between 12 and 42 per cent of λ, so it is a certificate rather than an estimate of the eigenvalue.10⁻³10⁻²10⁻¹10246810size of the negative eigenvalue, −λproducts before the test firesharder to find, and milder8 spectra, n = 40products at the largest λ3products at the smallest9smallest share of λ recovered0.12largest0.42the one that hidesis the one that matters least

A 40×40 matrix whose spectrum is positive over one decade apart from a single eigenvalue at −λ. At λ = 3 conjugate gradients meets a non-positive curvature after 3 products; at λ = 0.001 it takes 9. The trend is monotone and it runs the reassuring way: a Krylov space finds large eigenvalues first, so the indefiniteness that takes longest to detect is the indefiniteness that matters least. The direction that fires the test recovers between 12 and 42 per cent of λ, so it is a certificate rather than an estimate of the eigenvalue.

n: 40

The arguments are the ones The division that cannot be done passes. A value drawn at the generator's defaults instead would be a picture no essay asked for and no assertion has been run against.

Matrix–vector products before the curvature test fires, against the size of the negative eigenvalue, n = 40A 40×40 matrix whose spectrum is positive over one decade apart from a single eigenvalue at −λ. At λ = 3 conjugate gradients meets a non-positive curvature after 3 products; at λ = 0.001 it takes 9. The trend is monotone and it runs the reassuring way: a Krylov space finds large eigenvalues first, so the indefiniteness that takes longest to detect is the indefiniteness that matters least. The direction that fires the test recovers between 12 and 42 per cent of λ, so it is a certificate rather than an estimate of the eigenvalue.10⁻³10⁻²10⁻¹10246810size of the negative eigenvalue, −λproducts before the test firesharder to find, and milder8 spectra, n = 40products at the largest λ3products at the smallest9smallest share of λ recovered0.12largest0.42the one that hidesis the one that matters least

A 40×40 matrix whose spectrum is positive over one decade apart from a single eigenvalue at −λ. At λ = 3 conjugate gradients meets a non-positive curvature after 3 products; at λ = 0.001 it takes 9. The trend is monotone and it runs the reassuring way: a Krylov space finds large eigenvalues first, so the indefiniteness that takes longest to detect is the indefiniteness that matters least. The direction that fires the test recovers between 12 and 42 per cent of λ, so it is a certificate rather than an estimate of the eigenvalue.

n: 20

The arguments are the ones The division that cannot be done passes. A value drawn at the generator's defaults instead would be a picture no essay asked for and no assertion has been run against.

Matrix–vector products before the curvature test fires, against the size of the negative eigenvalue, n = 20A 20×20 matrix whose spectrum is positive over one decade apart from a single eigenvalue at −λ. At λ = 3 conjugate gradients meets a non-positive curvature after 3 products; at λ = 0.001 it takes 11. The trend is monotone and it runs the reassuring way: a Krylov space finds large eigenvalues first, so the indefiniteness that takes longest to detect is the indefiniteness that matters least. The direction that fires the test recovers between 6 and 33 per cent of λ, so it is a certificate rather than an estimate of the eigenvalue.10⁻³10⁻²10⁻¹1024681012size of the negative eigenvalue, −λproducts before the test firesharder to find, and milder8 spectra, n = 20products at the largest λ3products at the smallest11smallest share of λ recovered0.064largest0.33the one that hidesis the one that matters least

A 20×20 matrix whose spectrum is positive over one decade apart from a single eigenvalue at −λ. At λ = 3 conjugate gradients meets a non-positive curvature after 3 products; at λ = 0.001 it takes 11. The trend is monotone and it runs the reassuring way: a Krylov space finds large eigenvalues first, so the indefiniteness that takes longest to detect is the indefiniteness that matters least. The direction that fires the test recovers between 6 and 33 per cent of λ, so it is a certificate rather than an estimate of the eigenvalue.

n: 60

The arguments are the ones The division that cannot be done passes. A value drawn at the generator's defaults instead would be a picture no essay asked for and no assertion has been run against.

Matrix–vector products before the curvature test fires, against the size of the negative eigenvalue, n = 60A 60×60 matrix whose spectrum is positive over one decade apart from a single eigenvalue at −λ. At λ = 3 conjugate gradients meets a non-positive curvature after 4 products; at λ = 0.001 it takes 11. The trend is monotone and it runs the reassuring way: a Krylov space finds large eigenvalues first, so the indefiniteness that takes longest to detect is the indefiniteness that matters least. The direction that fires the test recovers between 0 and 29 per cent of λ, so it is a certificate rather than an estimate of the eigenvalue.10⁻³10⁻²10⁻¹1024681012size of the negative eigenvalue, −λproducts before the test firesharder to find, and milder8 spectra, n = 60products at the largest λ4products at the smallest11smallest share of λ recovered8.4·10⁻⁴largest0.29the one that hidesis the one that matters least

A 60×60 matrix whose spectrum is positive over one decade apart from a single eigenvalue at −λ. At λ = 3 conjugate gradients meets a non-positive curvature after 4 products; at λ = 0.001 it takes 11. The trend is monotone and it runs the reassuring way: a Krylov space finds large eigenvalues first, so the indefiniteness that takes longest to detect is the indefiniteness that matters least. The direction that fires the test recovers between 0 and 29 per cent of λ, so it is a certificate rather than an estimate of the eigenvalue.

What it checked while drawing

Every figure above asserted its own claims on the way to being drawn, and a claim that failed would have failed the build rather than drawn a wrong picture. Those assertions used to leave no trace at all: a passing one returned true and the only evidence the figure had checked anything was that nothing crashed. The list below is what they actually said, collected by running this generator with an observer installed — not a description of what it is believed to check.

12 distinct claims across 4 sets of arguments, grouped below by shape — because most of them are one sentence with a different number in it, and how many separate times that sentence was put to the test is the informative part.

a negative eigenvalue of -3 is found at all — asserted 8 times

a size the whole sweep can be run at

and a large one is found sooner than a small one

and at least one of them a substantial share

and the direction always recovers some share of the eigenvalue

Against the rule

It draws a decomposition and prints its residual. It calls detectionSweep, and every figure above carries the badge — which residualcheck verifies by looking for it in the emitted SVG rather than by finding the call that builds one. A badge that is constructed and then left out of the body is the failure that check exists for.

Across the library: the rule bites on 113 of 219 generators — 98 print a residual and 15 are exempt with a published reason; 106 factorise nothing. Read from lib/residual-rule.js, which is the same body the gate enforces from, and the gate's last check fails the build if this page and it disagree about any generator.

Where it is called

Changing this generator changes every figure on this list. That is what makes the list worth publishing rather than keeping in a check script.

The whole library · All essays · What must fail