How much a perturbation of the right-hand side is amplified, κ = 10⁶
At its defaults it draws how much a perturbation of the right-hand side is amplified, κ = 10⁶. The cumulative distribution of the amplification factor over two hundred random perturbation directions, with the condition number marked as the upper limit.
condition-amplify is one function in lib/figures/error.js —
error — the backward one, the forward one, and the number between them. 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.
The cumulative distribution of the amplification factor over two hundred random perturbation directions, with the condition number marked as the upper limit.
kappa: 1000000
The arguments are the ones A condition number for one eigenvalue passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
The cumulative distribution of the amplification factor over two hundred random perturbation directions, with the condition number marked as the upper limit.
kappa: 100
The arguments are the ones The exact answer to a nearby problem passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
The cumulative distribution of the amplification factor over two hundred random perturbation directions, with the condition number marked as the upper limit.
kappa: 10000
The arguments are the ones The exact answer to a nearby problem passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
The cumulative distribution of the amplification factor over two hundred random perturbation directions, with the condition number marked as the upper limit.
kappa: 10000000
The arguments are the ones The exact answer to a nearby problem passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
The cumulative distribution of the amplification factor over two hundred random perturbation directions, with the condition number marked as the upper limit.
kappa: 100000000
The arguments are the ones The exact answer to a nearby problem passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.
The cumulative distribution of the amplification factor over two hundred random perturbation directions, with the condition number marked as the upper limit.
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 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.
and even the mildest of the two hundred is amplified substantially
and two hundred random directions find a third of it
LU is for square matrices
matmul shapes agree
no perturbation is amplified by more than κ
the median direction is within an order of magnitude of the worst case
while still being below it
Against the rule
It calls a factoriser without drawing a factorisation
(solve),
so the rule is written down as not applying, with the reason:
uses the SVD only to obtain κ for the axis
The exemption list is the interesting half of the rule rather than an escape hatch — it is
where a decision about a figure had to be argued in one line. residualcheck
refuses an exemption that is not doing work, and rejected ten of the fifteen written for the
expansion's figures on exactly that ground: a figure whose vertical axis is a residual
satisfies the rule by construction, and touching a factoriser does not by itself require an
entry.
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 condition number for one eigenvalue
In the symmetric case every eigenvalue has condition number exactly one. In this four-by-four matrix two of them have condition number 100.005 and the other two have exactly 1, and the number belongs to the eigenvalue rather than to the matrix.
Iterating, instead of factorisingChanging the condition number on purpose
Preconditioning is usually introduced as a trick that makes an iteration converge faster. It is not a trick. It is solving a different system with the same solution and a condition number chosen rather than inherited, and the new condition number is computable.
Two errors, and whose fault they areThe condition number is an amplifier
κ is usually introduced as a definition and then quoted. It is a measurement: perturb the input by a known amount, look at how much the output moves, and the largest ratio you can find is the number.
Two errors, and whose fault they areThe exact answer to a nearby problem
A good algorithm does not give an approximate answer to your problem. It gives the exact answer to a problem very close to yours — and once that is the definition, a wrong result has two possible authors and they can be measured apart.