Six is the degree, and the model reaches it from 24 numbers
At its defaults it draws six is the degree, and the model reaches it from 24 numbers. A reduced model of a forty-state system built out of 24 samples of its transfer function and nothing else — no state matrix, no eigenvalues, no factorisation of anything the model is of. The Loewner pencil of divided differences is formed from the samples, its singular values are read for the degree (6 here, with a gap of 2.01·10⁸ across the cliff), and the pencil is projected onto that many directions. The filled curve is the worst relative error at the samples the model was built from; the open one is the worst error at forty points it never saw. At the true degree of six the first is 6.99·10⁻¹² — interpolation is exact by construction, not by accuracy — and the second is 2.12·10⁻⁹.
loewner-model is one function in lib/figures/graphfit.js —
three approximations the data chose the shape of — support points, a restricted perturbation, and a model made of measurements. 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 reduced model of a forty-state system built out of 24 samples of its transfer function and nothing else — no state matrix, no eigenvalues, no factorisation of anything the model is of. The Loewner pencil of divided differences is formed from the samples, its singular values are read for the degree (6 here, with a gap of 2.01·10⁸ across the cliff), and the pencil is projected onto that many directions. The filled curve is the worst relative error at the samples the model was built from; the open one is the worst error at forty points it never saw. At the true degree of six the first is 6.99·10⁻¹² — interpolation is exact by construction, not by accuracy — and the second is 2.12·10⁻⁹.
logNoise: -16
The arguments are the ones A model with no matrices behind it passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
A reduced model of a forty-state system built out of 24 samples of its transfer function and nothing else — no state matrix, no eigenvalues, no factorisation of anything the model is of. The Loewner pencil of divided differences is formed from the samples, its singular values are read for the degree (6 here, with a gap of 2.01·10⁸ across the cliff), and the pencil is projected onto that many directions. The filled curve is the worst relative error at the samples the model was built from; the open one is the worst error at forty points it never saw. At the true degree of six the first is 6.99·10⁻¹² — interpolation is exact by construction, not by accuracy — and the second is 2.12·10⁻⁹.
logNoise: -12
The arguments are the ones A model with no matrices behind it passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
A reduced model of a forty-state system built out of 24 samples of its transfer function and nothing else — no state matrix, no eigenvalues, no factorisation of anything the model is of. The Loewner pencil of divided differences is formed from the samples, its singular values are read for the degree (6 here, with a gap of 4.73·10⁶ across the cliff), and the pencil is projected onto that many directions. The filled curve is the worst relative error at the samples the model was built from; the open one is the worst error at forty points it never saw. With relative noise of 10⁻¹² on the samples the singular values stop falling to the rounding level, the cliff becomes a slope, and no order does better than 1.39·10⁻⁹ — the data's own error is a floor, and taking more of the degree past 11 buys nothing.
logNoise: -10
The arguments are the ones A model with no matrices behind it passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
A reduced model of a forty-state system built out of 24 samples of its transfer function and nothing else — no state matrix, no eigenvalues, no factorisation of anything the model is of. The Loewner pencil of divided differences is formed from the samples, its singular values are read for the degree (9 here, with a gap of 4.41 across the cliff), and the pencil is projected onto that many directions. The filled curve is the worst relative error at the samples the model was built from; the open one is the worst error at forty points it never saw. With relative noise of 10⁻¹⁰ on the samples the singular values stop falling to the rounding level, the cliff becomes a slope, and no order does better than 1.63·10⁻⁷ — the data's own error is a floor, and taking more of the degree past 11 buys nothing.
logNoise: -8
The arguments are the ones A model with no matrices behind it passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
A reduced model of a forty-state system built out of 24 samples of its transfer function and nothing else — no state matrix, no eigenvalues, no factorisation of anything the model is of. The Loewner pencil of divided differences is formed from the samples, its singular values are read for the degree (10 here, with a gap of 6561 across the cliff), and the pencil is projected onto that many directions. The filled curve is the worst relative error at the samples the model was built from; the open one is the worst error at forty points it never saw. With relative noise of 10⁻⁸ on the samples the singular values stop falling to the rounding level, the cliff becomes a slope, and no order does better than 1.64·10⁻⁵ — the data's own error is a floor, and taking more of the degree past 11 buys nothing.
logNoise: -4
The arguments are the ones A model with no matrices behind it passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
A reduced model of a forty-state system built out of 24 samples of its transfer function and nothing else — no state matrix, no eigenvalues, no factorisation of anything the model is of. The Loewner pencil of divided differences is formed from the samples, its singular values are read for the degree (12 here, with a gap of ∞ across the cliff), and the pencil is projected onto that many directions. The filled curve is the worst relative error at the samples the model was built from; the open one is the worst error at forty points it never saw. With relative noise of 10⁻⁴ on the samples the singular values stop falling to the rounding level, the cliff becomes a slope, and no order does better than 0.19 — the data's own error is a floor, and taking more of the degree past 11 buys nothing.
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.
17 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 degree the sample set can carry
a degree the state dimension can carry
a grid fine enough to have modes and coarse enough to draw
a noise level between the rounding and a per cent
a stable system
a system the measurement defines
a system this measurement defines
an actuator and a sensor on the grid
an even number of samples, at least four
an interval of real points to the left of a stable spectrum
an order the dial offers
an order the pencil can carry
and the model of that order passes through every sample
LU is for square matrices
matmul shapes agree
the best model is no better than the noise in its data
the singular values of the Loewner matrix find the degree
Against the rule
It draws a decomposition and prints its residual. It calls
orderSweep, orderSweep,
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 model with no matrices behind it
Twenty-four numbers — values of a transfer function at twenty-four points — produce a sixth-order model of a forty-state system that passes through every sample to 10⁻¹² and matches the function it never saw to 10⁻⁹. Noise of 10⁻¹⁰ on those numbers takes the rank decision's gap from 10⁸ to 4.
Reduction, and what a model is forA pole between the samples
A Loewner model needs a check on its error, and the one predicted to be cheap is four points chosen where the model disagrees most with the model one order higher. With exact samples it is better than predicted: on four systems the true error at those four points is the model's error within two per cent at every order, and the disagreement alone, with no sample at all, within three per cent at every order but the last. With noisy samples the checks keep working on every model that has no pole between the samples, and seventeen of thirty-two models do. Past the order the noise allows, the projection puts a real pole inside the sampled interval, so the error is infinite at one point and small a few per cent of a gap away, and every finite set of checks reports a number between 130 and 49,000 times too small. The test that catches every such model costs nothing: the model's own poles.
Reduction, and what a model is forAn error estimate made of samples
A Loewner model built from samples of a transfer function comes with no error bound, and the number every code reads to choose its order — the pencil's next singular value — is the obvious stand-in. On four systems sampled at 48 points it is the error of the order-k model for the first two orders, within a factor of 2.5, and then falls away from it by about a decade an order: by the fifth it is optimistic by 109, 181 and 457, and on one system by 6,700 at the sixth. An AAA fit of the same degree to the same samples has an error within a factor of six of the Loewner model's at every order, so the shortfall is not the projection's. The model's misfit at its own samples is optimistic the same way. What works is more samples: the error at the 47 midpoints between the samples is within a factor of 2.2 of the true error at every order on every system, and the four midpoints nearest the poles alone within a factor of three.