The 16 eigenvalues of an 8 × 8 quadratic eigenvalue problem, computed and in closed form
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 matrix that depends on its own eigenvalue passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked 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 nobody placed would be a picture no essay asked for and no claim was ever checked 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: 0.05
The arguments are the ones A matrix that depends on its own eigenvalue passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked 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.
beta: 0.3
The arguments are the ones A matrix that depends on its own eigenvalue passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
λ²M + λC + K for a chain of 8 masses with C = 0.3M + 0.3K. 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 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 3.05·10⁻¹⁵, and the routes share nothing but the three coefficient matrices.
beta: 1
The arguments are the ones A matrix that depends on its own eigenvalue passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
λ²M + λC + K for a chain of 8 masses with C = 0.3M + 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 12 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 9.24·10⁻¹⁵, and the routes share nothing but the three coefficient matrices.
What it checked while drawing
Every figure above checked its own claims on the way to being drawn, and a claim that failed
would have stopped the picture rather than shipped a wrong one. Those checks 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 217
of 397 generators —
199 print a residual and
18 are exempt with a published reason;
180 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 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 linearAn estimate that does not move
The tropical roots are said to miss the bottom of a spectrum by a factor growing like n². The estimate does not get worse. It changes by four per cent between four masses and sixty-four while the modulus it names falls by a factor of a hundred and sixty, and an estimate that is constant in the variable the answer depends on is a different defect from an inaccurate one.
The eigenvalue problem that is not linearOne mass removed, and one eigenvalue gone
A coordinate with no inertia reads like a coordinate that has been deleted, and a chain of eight masses with one of them removed would then be a chain of seven, with fourteen eigenvalues. It has fifteen. The massless coordinate is still there, still carrying a damper, and it contributes a first-order equation rather than none.