strength-graph
At its defaults it draws strong couplings and the points the matrix kept, ε = 1. A grid of points with lines drawn between the pairs the matrix couples strongly, and the points the coarsening kept drawn larger. At small anisotropy only the vertical lines remain and whole rows are kept.
strength-graph is one function in lib/figures/amg.js —
algebraic multigrid — a hierarchy with no grid behind it, and what it costs. 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.
A grid of points with lines drawn between the pairs the matrix couples strongly, and the points the coarsening kept drawn larger. At small anisotropy only the vertical lines remain and whole rows are kept.
eps: 0.001
The arguments are the ones Changing the condition number on purpose 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.
A grid of points with lines drawn between the pairs the matrix couples strongly, and the points the coarsening kept drawn larger. At small anisotropy only the vertical lines remain and whole rows are kept.
eps: 1
The arguments are the ones The coarse grid the matrix chooses 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.
A grid of points with lines drawn between the pairs the matrix couples strongly, and the points the coarsening kept drawn larger. At small anisotropy only the vertical lines remain and whole rows are kept.
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.
3 distinct claims across 3 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 grid small enough for its couplings to be drawn
and below the threshold the weak one is not strong at all
the strong direction is always strong
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 52
of 99 generators —
37 print a residual and
15 are exempt with a published reason;
47 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.
Changing 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.
Iterating, instead of factorisingThe coarse grid the matrix chooses
Given a tridiagonal matrix and no information about a grid, the coarsening keeps every other point and derives the weights ½, 1, ½ — the operators the geometric method was handed. Given the anisotropic operator, it discovers semi-coarsening, in the right direction, without a coordinate.
Sparsity, and what elimination costsTwo ends of the same arrow
One matrix, one row moved from the front of the elimination order to the back, and the factor goes from completely dense to no fill at all. Both factorisations are exact to rounding, and nothing numerical chose between them.