The filter 8 conjugate gradient steps apply, measured and predicted
At its defaults it draws the filter 8 conjugate gradient steps apply, measured and predicted. Filter factors against the singular-value index. The factors measured off the iterate and the polynomial predicted from the recurrence coefficients agree to 6.9·10⁻¹⁴ and are drawn as one curve. It rises above one — 1.070 at its largest — and changes direction several times. Tikhonov's filter at the matching cutoff is monotone and never exceeds one.
cg-filter-shape is one function in lib/figures/krylovreg.js —
iterative regularisation — a step count as the parameter, and the filter it applies. 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.
Filter factors against the singular-value index. The factors measured off the iterate and the polynomial predicted from the recurrence coefficients agree to 6.9·10⁻¹⁴ and are drawn as one curve. It rises above one — 1.070 at its largest — and changes direction several times. Tikhonov's filter at the matching cutoff is monotone and never exceeds one.
m: 16
The arguments are the ones A parameter that counts steps passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
Filter factors against the singular-value index. The factors measured off the iterate and the polynomial predicted from the recurrence coefficients agree to 1.2·10⁻¹¹ and are drawn as one curve. It rises above one — 1.203 at its largest — and changes direction several times. Tikhonov's filter at the matching cutoff is monotone and never exceeds one.
m: 8
The arguments are the ones The basis decides what a filter is passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
Filter factors against the singular-value index. The factors measured off the iterate and the polynomial predicted from the recurrence coefficients agree to 6.9·10⁻¹⁴ and are drawn as one curve. It rises above one — 1.070 at its largest — and changes direction several times. Tikhonov's filter at the matching cutoff is monotone and never exceeds one.
m: 2
The arguments are the ones The basis decides what a filter is passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
Filter factors against the singular-value index. The factors measured off the iterate and the polynomial predicted from the recurrence coefficients agree to 8.1·10⁻¹⁴ and are drawn as one curve. It rises above one — 1.104 at its largest — and changes direction several times. Tikhonov's filter at the matching cutoff is monotone and never exceeds one.
m: 4
The arguments are the ones The basis decides what a filter is passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
Filter factors against the singular-value index. The factors measured off the iterate and the polynomial predicted from the recurrence coefficients agree to 6.2·10⁻¹⁴ and are drawn as one curve. It rises above one — 1.015 at its largest — and changes direction several times. Tikhonov's filter at the matching cutoff is monotone and never exceeds one.
m: 12
The arguments are the ones The basis decides what a filter is passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
Filter factors against the singular-value index. The factors measured off the iterate and the polynomial predicted from the recurrence coefficients agree to 10⁻¹³ and are drawn as one curve. It rises above one — 1.120 at its largest — and changes direction several times. Tikhonov's filter at the matching cutoff is monotone and never exceeds one.
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.
6 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 size the SVD is affordable at
a step count the filter is still a filter at
Jacobi needs a symmetric matrix
most components have a coefficient worth dividing by
the measured and predicted factors agree at this step
Tikhonov's factors stay inside [0, 1]
Against the rule
It draws a decomposition and prints its residual. It calls
cgls,
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 parameter that counts steps
The regularisation field's knob is a positive real number chosen by one of three rules. The iterative field's is an integer nobody called a knob — where to stop. On the same problem the best step is 20 and the best λ is 0.025, and they reach 0.1426 and 0.1406.
Regularisation, and the answer that is chosenThe basis decides what a filter is
The vocabulary of regularisation is spectral — a method keeps a component or discards it, and the weights are a function of the singular value. Row-normalising a symmetric blur so that it preserves a constant makes it 8.6% asymmetric, and that is enough to move GMRES's weights from 7·10⁻¹⁴ off a function of σ to 4.4·10⁻².