tolerance-agreement
At its defaults it draws asking for more accuracy buys accuracy, and buys no agreement at all. Conjugate gradients on a 200 × 200 matrix with κ = 10⁴, solved at four tolerances, seven partition counts each. The bar at each tolerance spans the smallest and largest forward error the seven runs produced. The bars fall by 1.5·10⁶ across the sweep, which is the tolerance doing exactly what it is for. The ratio between the top and bottom of each bar is 1.34, 1.48, 1.71, 1.17, which does not fall with the tolerance. So the disagreement between machines is not a residue of an insufficiently converged answer that a tighter tolerance would remove; it is proportional to whatever accuracy was reached, and the runs stay a fixed factor apart all the way down.
tolerance-agreement is one function in lib/figures/race.js —
what a reduction decides when the decision is discrete — a step count, a rank, and a test's tolerance. 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.
Conjugate gradients on a 200 × 200 matrix with κ = 10⁴, solved at four tolerances, seven partition counts each. The bar at each tolerance spans the smallest and largest forward error the seven runs produced. The bars fall by 1.5·10⁶ across the sweep, which is the tolerance doing exactly what it is for. The ratio between the top and bottom of each bar is 1.34, 1.48, 1.71, 1.17, which does not fall with the tolerance. So the disagreement between machines is not a residue of an insufficiently converged answer that a tighter tolerance would remove; it is proportional to whatever accuracy was reached, and the runs stay a fixed factor apart all the way down.
n: 200
The arguments are the ones A stopping test is a race passes. A value drawn at the generator's defaults instead would be a picture no essay asked for and no assertion has been run against.
Conjugate gradients on a 200 × 200 matrix with κ = 10⁴, solved at four tolerances, seven partition counts each. The bar at each tolerance spans the smallest and largest forward error the seven runs produced. The bars fall by 1.5·10⁶ across the sweep, which is the tolerance doing exactly what it is for. The ratio between the top and bottom of each bar is 1.34, 1.48, 1.71, 1.17, which does not fall with the tolerance. So the disagreement between machines is not a residue of an insufficiently converged answer that a tighter tolerance would remove; it is proportional to whatever accuracy was reached, and the runs stay a fixed factor apart all the way down.
n: 80
The arguments are the ones The tolerance that buys no agreement passes. A value drawn at the generator's defaults instead would be a picture no essay asked for and no assertion has been run against.
Conjugate gradients on a 80 × 80 matrix with κ = 10⁴, solved at four tolerances, seven partition counts each. The bar at each tolerance spans the smallest and largest forward error the seven runs produced. The bars fall by 5.4·10⁶ across the sweep, which is the tolerance doing exactly what it is for. The ratio between the top and bottom of each bar is 1.04, 4.83, 14.83, 1.36, which does not fall with the tolerance. So the disagreement between machines is not a residue of an insufficiently converged answer that a tighter tolerance would remove; it is proportional to whatever accuracy was reached, and the runs stay a fixed factor apart all the way down.
n: 300
The arguments are the ones The tolerance that buys no agreement passes. A value drawn at the generator's defaults instead would be a picture no essay asked for and no assertion has been run against.
Conjugate gradients on a 300 × 300 matrix with κ = 10⁴, solved at four tolerances, seven partition counts each. The bar at each tolerance spans the smallest and largest forward error the seven runs produced. The bars fall by 8.7·10⁵ across the sweep, which is the tolerance doing exactly what it is for. The ratio between the top and bottom of each bar is 1.27, 1.36, 1.11, 1.38, which does not fall with the tolerance. So the disagreement between machines is not a residue of an insufficiently converged answer that a tighter tolerance would remove; it is proportional to whatever accuracy was reached, and the runs stay a fixed factor apart all the way down.
What it checked while drawing
Every figure above asserted its own claims on the way to being drawn, and a claim that failed
would have failed the build rather than drawn a wrong picture. Those assertions 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 4 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 problem the sweep can solve twenty-eight times
and the disagreement between runs does not close
every tolerance shows a spread of iteration counts
LU is for square matrices
matmul shapes agree
the accuracy improves by orders across the sweep
there is a disagreement to speak of at all
Against the rule
It draws a decomposition and prints its residual. It calls
toleranceSweep,
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 197
of 363 generators —
179 print a residual and
18 are exempt with a published reason;
166 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 stopping test is a race
One matrix, one right-hand side, one tolerance, thirteen partition counts — and eleven different iteration counts between 674 and 690. Every run converged, every answer is right to the accuracy asked for, and what differs is the bill.
The answer that depends on the machineThe tolerance that buys no agreement
Ask for four more orders of accuracy and you get them — the answers improve by a factor of 1.5 million. The ratio between the best and the worst run is 1.34, 1.48, 1.71 and 1.17 across the same sweep. The band falls and it does not close.
The answer that depends on the machineWhat a regression test can ask for
The machine's own variation on one solve is 3.2·10⁻¹², and the smallest defect whose answers clear it is one part in 10¹². The tolerance exists, it is bracketed on both sides by a factor of 1.42, and it is neither zero nor the 10⁻⁸ that usually gets typed.