double-root-loss
At its defaults it draws a double root, approached: the pair separates like √ε and the accuracy fails like √u. A chain of 8 masses at β = β*(1 + ε), where β* is the critical damping and the smallest mode's two eigenvalues coincide at ε = 0. The separation of that pair is 0.9657√ε at every one of twelve decades — a spread of 1.02 in the constant — and the computed spectrum's worst relative error against the closed form rises as the pair closes, reaching 3.732·10⁻⁸ at the boundary itself. That is √u times a small constant: half the digits, on a problem where κ(K) is 32.16 at every stop and the coefficients are integers. Nothing here is ill conditioned in any sense this site has used before; what is unbounded is the derivative of the map from a coefficient to a double root.
double-root-loss is one function in lib/figures/hyperbolic.js —
real by class — a cholesky that certifies a spectrum, and a double root that costs half the digits. 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.
A chain of 8 masses at β = β*(1 + ε), where β* is the critical damping and the smallest mode's two eigenvalues coincide at ε = 0. The separation of that pair is 0.9657√ε at every one of twelve decades — a spread of 1.02 in the constant — and the computed spectrum's worst relative error against the closed form rises as the pair closes, reaching 3.732·10⁻⁸ at the boundary itself. That is √u times a small constant: half the digits, on a problem where κ(K) is 32.16 at every stop and the coefficients are integers. Nothing here is ill conditioned in any sense this site has used before; what is unbounded is the derivative of the map from a coefficient to a double root.
n: 8
The arguments are the ones A backward-stable answer to a problem nobody asked 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.
A chain of 8 masses at β = β*(1 + ε), where β* is the critical damping and the smallest mode's two eigenvalues coincide at ε = 0. The separation of that pair is 0.9657√ε at every one of twelve decades — a spread of 1.02 in the constant — and the computed spectrum's worst relative error against the closed form rises as the pair closes, reaching 3.732·10⁻⁸ at the boundary itself. That is √u times a small constant: half the digits, on a problem where κ(K) is 32.16 at every stop and the coefficients are integers. Nothing here is ill conditioned in any sense this site has used before; what is unbounded is the derivative of the map from a coefficient to a double root.
n: 4
The arguments are the ones Every eigenvalue real, and a test that says so 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.
A chain of 4 masses at β = β*(1 + ε), where β* is the critical damping and the smallest mode's two eigenvalues coincide at ε = 0. The separation of that pair is 1.75√ε at every one of twelve decades — a spread of 1.002 in the constant — and the computed spectrum's worst relative error against the closed form rises as the pair closes, reaching 2.855·10⁻⁸ at the boundary itself. That is √u times a small constant: half the digits, on a problem where κ(K) is 9.472 at every stop and the coefficients are integers. Nothing here is ill conditioned in any sense this site has used before; what is unbounded is the derivative of the map from a coefficient to a double root.
n: 16
The arguments are the ones Every eigenvalue real, and a test that says so 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.
A chain of 16 masses at β = β*(1 + ε), where β* is the critical damping and the smallest mode's two eigenvalues coincide at ε = 0. The separation of that pair is 0.5162√ε at every one of twelve decades — a spread of 1.014 in the constant — and the computed spectrum's worst relative error against the closed form rises as the pair closes, reaching 1.085·10⁻⁷ at the boundary itself. That is √u times a small constant: half the digits, on a problem where κ(K) is 116.5 at every stop and the coefficients are integers. Nothing here is ill conditioned in any sense this site has used before; what is unbounded is the derivative of the map from a coefficient to a double root.
n: 6
The arguments are the ones Every eigenvalue real, and a test that says so 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.
A chain of 6 masses at β = β*(1 + ε), where β* is the critical damping and the smallest mode's two eigenvalues coincide at ε = 0. The separation of that pair is 1.26√ε at every one of twelve decades — a spread of 1.002 in the constant — and the computed spectrum's worst relative error against the closed form rises as the pair closes, reaching 4.55·10⁻⁸ at the boundary itself. That is √u times a small constant: half the digits, on a problem where κ(K) is 19.2 at every stop and the coefficients are integers. Nothing here is ill conditioned in any sense this site has used before; what is unbounded is the derivative of the map from a coefficient to a double root.
n: 12
The arguments are the ones Every eigenvalue real, and a test that says so 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.
A chain of 12 masses at β = β*(1 + ε), where β* is the critical damping and the smallest mode's two eigenvalues coincide at ε = 0. The separation of that pair is 0.6909√ε at every one of twelve decades — a spread of 1.013 in the constant — and the computed spectrum's worst relative error against the closed form rises as the pair closes, reaching 7.465·10⁻⁸ at the boundary itself. That is √u times a small constant: half the digits, on a problem where κ(K) is 67.83 at every stop and the coefficients are integers. Nothing here is ill conditioned in any sense this site has used before; what is unbounded is the derivative of the map from a coefficient to a double root.
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 6 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 chain long enough to have a spectrum and short enough to draw
a linearisation has as many eigenvalues as it has rows
a positive mass
a reduction this file knows
a size the closed form can be read at
and the double root costs about half the digits
damping that removes energy rather than adding it
every computed eigenvalue is matched to an unused exact one
LU is for square matrices
matmul shapes agree
the separation is a constant times √ε
two spectra of the same size
Against the rule
The rule does not apply to it. It factorises nothing, so there is no residual it could be withholding. That is worth stating rather than leaving blank: a site that reported the rule as satisfied by every generator would be counting mostly generators the rule never reached.
Across the library: the rule bites on 173
of 325 generators —
158 print a residual and
15 are exempt with a published reason;
152 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.
A backward-stable answer to a problem nobody asked
One quadratic eigenvalue problem, in nine systems of units, with a change of variable that is exact in both directions. The residual the solver prints stays at the rounding level at every stop. The answer loses eleven orders of magnitude, and the two facts are consistent.
The eigenvalue problem that is not linearA matrix that depends on its own eigenvalue
A damped structure does not produce Ax = λx. It produces (λ²M + λC + K)x = 0, where the matrix whose null vector is wanted is a function of the number being solved for — so there is nothing to factorise, an n × n problem has 2n answers, and the eigenvectors cannot be a basis.
The eigenvalue problem that is not linearA spectrum that comes in reciprocal pairs
A palindromic quadratic reads the same backwards, so λ is an eigenvalue exactly when 1/λ is. A general solver discards that, computes the large half of the spectrum perfectly and the small half to seven digits — and the small half is a division away from being perfect too.
The eigenvalue problem that is not linearEvery eigenvalue real, and a test that says so
A quadratic eigenvalue problem has no reason to have real eigenvalues. One class does, as a property rather than an outcome, and the proof is a Cholesky that completes. The boundary of the class has a closed form, and at the boundary the arithmetic loses half its digits with nothing ill conditioned anywhere.
The eigenvalue problem that is not linearSix routes to one spectrum
Three linearisations of one quadratic, each reduced to a standard eigenvalue problem two ways. All six have exactly the same eigenvalues in exact arithmetic. On a well-scaled problem they differ by noise; on a badly scaled one by a factor of forty; and two of the six are the same matrix.
The eigenvalue problem that is not linearThe scaling that buys ten orders
Two lines computed from three norms, a change of variable that is exact in both directions, and the whole of the loss the previous essay measured comes back — flat, at every stop, because after scaling every stop is the same problem.