Generator

aggregate-map

One function in the aggregation library, called 6 times across 5 essays. Below: what it draws at its defaults, what it draws at every value an essay asks for, the 4 claims it put to the test while drawing them, and where it stands against the rule this site is named for.

At its defaults it draws the aggregates the matrix chooses at ε = 0.001. A 15×15 grid of unknowns, each tinted by the aggregate that owns it. The operator's couplings are 1.0e-3 along x and 1.000 along y, so the strong direction is y — and the aggregates come out 3.00 points tall on average and 1.00 wide. 75 aggregates cover 225 points, 75 of them opened by the first pass and 15 points handed out by the second.

aggregate-map is one function in lib/figures/aggregation.js — smoothed aggregation — the sweep that is the method, and the angle that defeats it. 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 aggregates the matrix chooses at ε = 0.001A 15×15 grid of unknowns, each tinted by the aggregate that owns it. The operator's couplings are 1.0e-3 along x and 1.000 along y, so the strong direction is y — and the aggregates come out 3.00 points tall on average and 1.00 wide. 75 aggregates cover 225 points, 75 of them opened by the first pass and 15 points handed out by the second.75 aggregates over 225 unknownsthe matrix chose thismean extent along y3mean extent along x1points adopted by pass two15no coordinate enters the methodand the shape follows the coupling

A 15×15 grid of unknowns, each tinted by the aggregate that owns it. The operator's couplings are 1.0e-3 along x and 1.000 along y, so the strong direction is y — and the aggregates come out 3.00 points tall on average and 1.00 wide. 75 aggregates cover 225 points, 75 of them opened by the first pass and 15 points handed out by the second.

eps: 0.01

The arguments are the ones A hierarchy with no grid behind it 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 aggregates the matrix chooses at ε = 0.01A 15×15 grid of unknowns, each tinted by the aggregate that owns it. The operator's couplings are 1.0e-2 along x and 1.000 along y, so the strong direction is y — and the aggregates come out 3.00 points tall on average and 1.00 wide. 75 aggregates cover 225 points, 75 of them opened by the first pass and 15 points handed out by the second.75 aggregates over 225 unknownsthe matrix chose thismean extent along y3mean extent along x1points adopted by pass two15no coordinate enters the methodand the shape follows the coupling

A 15×15 grid of unknowns, each tinted by the aggregate that owns it. The operator's couplings are 1.0e-2 along x and 1.000 along y, so the strong direction is y — and the aggregates come out 3.00 points tall on average and 1.00 wide. 75 aggregates cover 225 points, 75 of them opened by the first pass and 15 points handed out by the second.

eps: 0.001

The arguments are the ones Aggregating what the matrix calls strong 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 aggregates the matrix chooses at ε = 0.001A 15×15 grid of unknowns, each tinted by the aggregate that owns it. The operator's couplings are 1.0e-3 along x and 1.000 along y, so the strong direction is y — and the aggregates come out 3.00 points tall on average and 1.00 wide. 75 aggregates cover 225 points, 75 of them opened by the first pass and 15 points handed out by the second.75 aggregates over 225 unknownsthe matrix chose thismean extent along y3mean extent along x1points adopted by pass two15no coordinate enters the methodand the shape follows the coupling

A 15×15 grid of unknowns, each tinted by the aggregate that owns it. The operator's couplings are 1.0e-3 along x and 1.000 along y, so the strong direction is y — and the aggregates come out 3.00 points tall on average and 1.00 wide. 75 aggregates cover 225 points, 75 of them opened by the first pass and 15 points handed out by the second.

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.

4 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 whose cells are large enough to see

an anisotropy the operator is defined at

and the aggregation coarsens by at least a half

every point is in exactly one aggregate

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 66 of 131 generators — 51 print a residual and 15 are exempt with a published reason; 65 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.

Iterating, instead of factorising

A hierarchy with no grid behind it

On a graph Laplacian the algebraic V-cycle converges at 0.199 a cycle, its grid complexity is an unremarkable 3.05, and its operator complexity is 17.7 — one level of forty-one unknowns is entirely dense. The number people quote is the one that does not measure the work.

Iterating, instead of factorising

Aggregating what the matrix calls strong

The depth phase measured every method it had on the 45°-rotated anisotropic operator — 0.784, 0.883, 0.844 — and diagnosed the failure as being in the discretisation rather than in the hierarchy. Smoothed aggregation is the standard answer to anisotropy. It returns 0.789.

Iterating, instead of factorising

Coarsening in one direction only

Leave the smoother alone and halve only the strong direction, and the smoothing factor is 0.3340 — identical to line relaxation's, at every anisotropy and every weight, to twelve digits. The convergence factors are then a factor of three apart, and at 45° both repairs fail outright.

Iterating, instead of factorising

The 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 costs

The order decides the memory

Four elimination orderings on one matrix give factors of 1,739, 1,354, 1,413 and 1,026 entries. All four factorisations are exact, all four return the same answer, and the one with the better asymptotics is not the one that wins.

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