Generator

Incomplete Cholesky on the 10×10 grid: κ 48.4 → 5.12

One function in the iterative library, called 9 times across 2 essays. Below: what it draws at its defaults, what it draws at every value an essay asks for, the 5 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 incomplete cholesky on the 10×10 grid: κ 48.4 → 5.12. A semi-logarithmic plot of relative residual against iteration for conjugate gradients with and without an incomplete Cholesky preconditioner, the preconditioned curve falling faster.

preconditioner-effect is one function in lib/figures/iterative.js — iterative — krylov and stationary methods against rates known in closed form. 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.

Incomplete Cholesky on the 10×10 grid: κ 48.4 → 5.12A semi-logarithmic plot of relative residual against iteration for conjugate gradients with and without an incomplete Cholesky preconditioner, the preconditioned curve falling faster.051015202530354010⁻¹¹10⁻⁹10⁻⁷10⁻⁵10⁻³10⁻¹iteration‖r‖ / ‖b‖plain CGIC(0) CGwhat the preconditioner didκ(A)48κ(L⁻¹AL⁻ᵀ)5.1‖A − LLᵀ‖/‖A‖0.0832D Laplacian, n = 100√κ ratio predicts 3.07×

A semi-logarithmic plot of relative residual against iteration for conjugate gradients with and without an incomplete Cholesky preconditioner, the preconditioned curve falling faster.

grid: 10

The arguments are the ones A rate that is known in advance passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.

Incomplete Cholesky on the 10×10 grid: κ 48.4 → 5.12A semi-logarithmic plot of relative residual against iteration for conjugate gradients with and without an incomplete Cholesky preconditioner, the preconditioned curve falling faster.051015202530354010⁻¹¹10⁻⁹10⁻⁷10⁻⁵10⁻³10⁻¹iteration‖r‖ / ‖b‖plain CGIC(0) CGwhat the preconditioner didκ(A)48κ(L⁻¹AL⁻ᵀ)5.1‖A − LLᵀ‖/‖A‖0.0832D Laplacian, n = 100√κ ratio predicts 3.07×

A semi-logarithmic plot of relative residual against iteration for conjugate gradients with and without an incomplete Cholesky preconditioner, the preconditioned curve falling faster.

grid: 6

The arguments are the ones Changing the condition number on purpose passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.

Incomplete Cholesky on the 6×6 grid: κ 19.2 → 2.54A semi-logarithmic plot of relative residual against iteration for conjugate gradients with and without an incomplete Cholesky preconditioner, the preconditioned curve falling faster.051015202510⁻¹¹10⁻⁹10⁻⁷10⁻⁵10⁻³10⁻¹iteration‖r‖ / ‖b‖plain CGIC(0) CGwhat the preconditioner didκ(A)19κ(L⁻¹AL⁻ᵀ)2.5‖A − LLᵀ‖/‖A‖0.0762D Laplacian, n = 36√κ ratio predicts 2.75×

A semi-logarithmic plot of relative residual against iteration for conjugate gradients with and without an incomplete Cholesky preconditioner, the preconditioned curve falling faster.

grid: 14

The arguments are the ones Changing the condition number on purpose passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.

Incomplete Cholesky on the 14×14 grid: κ 90.5 → 8.85A semi-logarithmic plot of relative residual against iteration for conjugate gradients with and without an incomplete Cholesky preconditioner, the preconditioned curve falling faster.0714212835424910⁻¹¹10⁻⁹10⁻⁷10⁻⁵10⁻³10⁻¹iteration‖r‖ / ‖b‖plain CGIC(0) CGwhat the preconditioner didκ(A)91κ(L⁻¹AL⁻ᵀ)8.8‖A − LLᵀ‖/‖A‖0.0852D Laplacian, n = 196√κ ratio predicts 3.20×

A semi-logarithmic plot of relative residual against iteration for conjugate gradients with and without an incomplete Cholesky preconditioner, the preconditioned curve falling faster.

grid: 9

The arguments are the ones Changing the condition number on purpose passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.

Incomplete Cholesky on the 9×9 grid: κ 39.9 → 4.37A semi-logarithmic plot of relative residual against iteration for conjugate gradients with and without an incomplete Cholesky preconditioner, the preconditioned curve falling faster.0510152025303510⁻¹¹10⁻⁹10⁻⁷10⁻⁵10⁻³10⁻¹iteration‖r‖ / ‖b‖plain CGIC(0) CGwhat the preconditioner didκ(A)40κ(L⁻¹AL⁻ᵀ)4.4‖A − LLᵀ‖/‖A‖0.0812D Laplacian, n = 81√κ ratio predicts 3.02×

A semi-logarithmic plot of relative residual against iteration for conjugate gradients with and without an incomplete Cholesky preconditioner, the preconditioned curve falling faster.

grid: 12

The arguments are the ones Changing the condition number on purpose passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.

Incomplete Cholesky on the 12×12 grid: κ 67.8 → 6.84A semi-logarithmic plot of relative residual against iteration for conjugate gradients with and without an incomplete Cholesky preconditioner, the preconditioned curve falling faster.061218243036424810⁻¹¹10⁻⁹10⁻⁷10⁻⁵10⁻³10⁻¹iteration‖r‖ / ‖b‖plain CGIC(0) CGwhat the preconditioner didκ(A)68κ(L⁻¹AL⁻ᵀ)6.8‖A − LLᵀ‖/‖A‖0.0842D Laplacian, n = 144√κ ratio predicts 3.15×

A semi-logarithmic plot of relative residual against iteration for conjugate gradients with and without an incomplete Cholesky preconditioner, the preconditioned curve falling faster.

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.

5 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 the iteration count falls with it

matmul shapes agree

the factorisation really is incomplete

the incomplete factorisation exists

the preconditioned condition number is smaller

Against the rule

It draws a decomposition and prints its residual. It calls ichol, 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 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.

The whole library · All essays · What must fail