Two penalties on the same problem, against the offset in the signal
At its defaults it draws two penalties on the same problem, against the offset in the signal. Best relative error for each penalty at four offsets. With no offset the two are within 7% of each other. At an offset of 10 the derivative penalty is 1.85 times better, because a constant lies in its null space and costs it nothing, while the norm penalty pays for the whole offset at every λ.
penalty-null-space 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.
Best relative error for each penalty at four offsets. With no offset the two are within 7% of each other. At an offset of 10 the derivative penalty is 1.85 times better, because a constant lies in its null space and costs it nothing, while the norm penalty pays for the whole offset at every λ.
n: 48
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.
Best relative error for each penalty at four offsets. With no offset the two are within 7% of each other. At an offset of 10 the derivative penalty is 1.85 times better, because a constant lies in its null space and costs it nothing, while the norm penalty pays for the whole offset at every λ.
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.
7 distinct claims across 2 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.
the derivative penalty's advantage grows at offset 2 — checked 3 times
a range of offsets wide enough to show the trend
a size the λ sweep is affordable at
enough noise for the penalty to matter
with almost nothing between them at no offset
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 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.
The matrix a constraint makesA preconditioner that need not know the constraint
Keep the constraint block exactly and replace the objective block by anything positive definite on the null space. The preconditioned matrix then has 2m eigenvalues at exactly one, and its remaining n − m are the generalised eigenvalues of a pencil in which the constraint does not appear. Sweep its condition number over six decades and they do not move in six digits.
Regularisation, and the answer that is chosenThe corner reads the norm it is drawn in
The L-curve was the costliest rule this field scored, and the cost was not the rule's. On the same sixty draws, with the same best achievable error, the corner of ‖x‖ against the residual costs 1.53 times the oracle and the corner of ‖L₁x‖ costs 1.003. Across five signals and three penalties the corner lands wherever amplified noise is between a tenth and a fifth of the norm being plotted, and it finds the oracle only when the oracle happens to sit there — twenty-nine times too costly on a smooth signal under ‖x‖, within half a per cent on four spikes.