qep-spectrum
At its defaults it draws the 16 eigenvalues of an 8 × 8 quadratic eigenvalue problem, computed and in closed form. λ²M + λC + K for a chain of 8 masses with C = 0.3M + 0.1K. The crosses are the closed form — one scalar quadratic per eigenvalue of K, whose roots are known exactly — and the discs are the eigenvalues a real Schur factorisation returns from the 16 × 16 first companion linearisation. There are 16 of them for a matrix with 8 rows, of which 16 are complex and arrive in conjugate pairs, so the eigenvectors cannot be independent: 9 vectors in 8 dimensions never are. The worst disagreement between the two routes is 2.12·10⁻¹⁵, and the routes share nothing but the three coefficient matrices.
qep-spectrum is one function in lib/figures/quadratic.js —
a matrix that depends on its own eigenvalue — 2n answers for n rows, and a count that is a degree. 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.
λ²M + λC + K for a chain of 8 masses with C = 0.3M + 0.1K. The crosses are the closed form — one scalar quadratic per eigenvalue of K, whose roots are known exactly — and the discs are the eigenvalues a real Schur factorisation returns from the 16 × 16 first companion linearisation. There are 16 of them for a matrix with 8 rows, of which 16 are complex and arrive in conjugate pairs, so the eigenvectors cannot be independent: 9 vectors in 8 dimensions never are. The worst disagreement between the two routes is 2.12·10⁻¹⁵, and the routes share nothing but the three coefficient matrices.
beta: 0.1
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.
λ²M + λC + K for a chain of 8 masses with C = 0.3M + 0.1K. The crosses are the closed form — one scalar quadratic per eigenvalue of K, whose roots are known exactly — and the discs are the eigenvalues a real Schur factorisation returns from the 16 × 16 first companion linearisation. There are 16 of them for a matrix with 8 rows, of which 16 are complex and arrive in conjugate pairs, so the eigenvectors cannot be independent: 9 vectors in 8 dimensions never are. The worst disagreement between the two routes is 2.12·10⁻¹⁵, and the routes share nothing but the three coefficient matrices.
beta: 0
The arguments are the ones A matrix that depends on its own eigenvalue 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.
λ²M + λC + K for a chain of 8 masses with C = 0.3M + 0K. The crosses are the closed form — one scalar quadratic per eigenvalue of K, whose roots are known exactly — and the discs are the eigenvalues a real Schur factorisation returns from the 16 × 16 first companion linearisation. There are 16 of them for a matrix with 8 rows, of which 16 are complex and arrive in conjugate pairs, so the eigenvectors cannot be independent: 9 vectors in 8 dimensions never are. The worst disagreement between the two routes is 4.14·10⁻¹⁵, and the routes share nothing but the three coefficient matrices.
beta: 3
The arguments are the ones A matrix that depends on its own eigenvalue 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.
λ²M + λC + K for a chain of 8 masses with C = 0.3M + 3K. The crosses are the closed form — one scalar quadratic per eigenvalue of K, whose roots are known exactly — and the discs are the eigenvalues a real Schur factorisation returns from the 16 × 16 first companion linearisation. There are 16 of them for a matrix with 8 rows, of which 2 are complex and arrive in conjugate pairs, so the eigenvectors cannot be independent: 9 vectors in 8 dimensions never are. The worst disagreement between the two routes is 8.65·10⁻¹⁵, and the routes share nothing but the three coefficient matrices.
n: 16
The arguments are the ones A matrix that depends on its own eigenvalue 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.
λ²M + λC + K for a chain of 16 masses with C = 0.3M + 0.1K. The crosses are the closed form — one scalar quadratic per eigenvalue of K, whose roots are known exactly — and the discs are the eigenvalues a real Schur factorisation returns from the 32 × 32 first companion linearisation. There are 32 of them for a matrix with 16 rows, of which 32 are complex and arrive in conjugate pairs, so the eigenvectors cannot be independent: 17 vectors in 16 dimensions never are. The worst disagreement between the two routes is 1.51·10⁻¹⁴, and the routes share nothing but the three coefficient matrices.
beta: 0.05
The arguments are the ones A matrix that depends on its own eigenvalue 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.
λ²M + λC + K for a chain of 8 masses with C = 0.3M + 0.05K. The crosses are the closed form — one scalar quadratic per eigenvalue of K, whose roots are known exactly — and the discs are the eigenvalues a real Schur factorisation returns from the 16 × 16 first companion linearisation. There are 16 of them for a matrix with 8 rows, of which 16 are complex and arrive in conjugate pairs, so the eigenvectors cannot be independent: 9 vectors in 8 dimensions never are. The worst disagreement between the two routes is 1.72·10⁻¹⁵, and the routes share nothing but the three coefficient matrices.
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.
13 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 chain whose spectrum fits on the plane
a linearisation has as many eigenvalues as it has rows
a positive mass
a reduction this file knows
and the linearisation reaches the closed form
damping that removes energy
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
twice as many eigenvalues as the matrix has rows
two spectra of the same size
Against the rule
It draws a decomposition and prints its residual. It calls
qepEigen,
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 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.
Iterating, instead of factorisingA Krylov space for a problem that is not linear
A quadratic eigenvalue problem has no matrix to build a Krylov space out of. The recurrence that builds one anyway stores half as many numbers, returns twice as many Ritz values — and stops being a basis at twenty vectors while the answer it gives keeps improving.
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 problem with infinitely many eigenvalues
Let the matrix depend on λ through something that is not a polynomial and three things stop being true at once. There is no linearisation, there is no characteristic polynomial, and "compute the spectrum" is not a request that can be granted — the only finite question is how many eigenvalues are inside this circle.
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.
Randomised, and the guarantee that changes kindCounting what is inside a circle
A trace of a matrix nobody wants to form, integrated around a contour, gives an integer — how many eigenvalues are inside. It converges exponentially, it is estimated with random probes, and the probe block is a ceiling that the answer does not mention.
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.
Two errors, and whose fault they areThe roots are not the coefficients
A polynomial whose roots are the integers one to twenty, expanded exactly, handed to the routine every library uses. The computed roots are wrong in the third digit, the computation is backward stable for the matrix it factorised, and above degree eighteen the coefficients are not double-precision numbers at all.
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.
The arithmetic underneathThe units that overflow before the answer does
A change of variable that is exact in the algebra requires γ² times a matrix to be a number the format can hold. In binary64 that is a bound nobody meets by accident. In binary32 it arrives at 10¹⁹ and in fp16 at 256, and past it there is no answer rather than a poor one.