curvature-trail
At its defaults it draws the curvature pᵀap/pᵀp along conjugate gradients on a matrix with one eigenvalue at −10^-1. The matrix is 40×40, positive definite apart from a single eigenvalue at -0.1. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.43, 2.72, 1.71, 0.687 before turning negative at step 6, where it is -0.02656. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 26.6 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 37 steps.
curvature-trail is one function in lib/figures/curvature.js —
negative curvature — the division that cannot be done, as an output. 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 matrix is 40×40, positive definite apart from a single eigenvalue at -0.1. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.43, 2.72, 1.71, 0.687 before turning negative at step 6, where it is -0.02656. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 26.6 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 37 steps.
logLambda: -1
The arguments are the ones The division that cannot be done 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.
The matrix is 40×40, positive definite apart from a single eigenvalue at -0.1. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.43, 2.72, 1.71, 0.687 before turning negative at step 6, where it is -0.02656. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 26.6 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 37 steps.
logLambda: 1
The arguments are the ones The division that cannot be done 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.
The matrix is 40×40, positive definite apart from a single eigenvalue at -10. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.09, -3.51 before turning negative at step 2, where it is -3.507. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 35.1 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 37 steps.
logLambda: -3
The arguments are the ones The division that cannot be done 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.
The matrix is 40×40, positive definite apart from a single eigenvalue at -0.001. Conjugate gradients divides by pᵀAp at every step, and the quantity runs 5.44, 2.76, 1.83, 0.884 before turning negative at step 9, where it is -2.811·10⁻⁴. That direction is a proof: one matrix–vector product from outside the iteration confirms it, and it recovers 28.1 per cent of the eigenvalue. MINRES on the same system never forms this quantity, meets nothing, and returns the solution in 39 steps.
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.
6 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 negative eigenvalue inside the range the sweep covers
a power of ten rather than an exponent literal
a size the repeated products can afford
and the direction it stopped on has negative curvature
conjugate gradients meets a non-positive curvature
while MINRES, which never forms the quantity, solves the system
Against the rule
It draws a decomposition and prints its residual. It calls
cgCurvature, minres,
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 113
of 219 generators —
98 print a residual and
15 are exempt with a published reason;
106 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.