Generator

congruence-bars

One function in the cpals library, called 11 times across 5 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 how much two runs of the same fit agree about the factors, from 6 starting points each. Every run here reaches its target to the rounding level — 3·10⁻¹³, 1.16·10⁻¹¹ and 2.39·10⁻¹⁵ at worst — so all three are successful factorisations. The bar is the worst agreement between any two of them about the *factors*, matched over permutations and scalings, which is exactly the freedom the uniqueness theorem allows. Kruskal's condition k_A + k_B + k_C ≥ 2r + 2 holds for the first (9 ≥ 8) and fails for the second (6 < 8), and the bars are 1.0000 and 0.0207. The matrix is the comparison the whole thing rests on: AB = (AM)(M⁻¹B) for every invertible M, so its runs agree to 0.1089 and its factors mean nothing on their own.

congruence-bars is one function in lib/figures/cpals.js — alternating least squares — an iteration that walks out of the set it is searching. 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.

How much two runs of the same fit agree about the factors, from 6 starting points eachEvery run here reaches its target to the rounding level — 3·10⁻¹³, 1.16·10⁻¹¹ and 2.39·10⁻¹⁵ at worst — so all three are successful factorisations. The bar is the worst agreement between any two of them about the *factors*, matched over permutations and scalings, which is exactly the freedom the uniqueness theorem allows. Kruskal's condition k_A + k_B + k_C ≥ 2r + 2 holds for the first (9 ≥ 8) and fails for the second (6 < 8), and the bars are 1.0000 and 0.0207. The matrix is the comparison the whole thing rests on: AB = (AM)(M⁻¹B) for every invertible M, so its runs agree to 0.1089 and its factors mean nothing on their own.6 × 6 × 6, rank 3 · 9 ≥ 81.00002 × 2 × 2, rank 3 · 6 < 80.02076 × 6 matrix, rank 3 · no condition0.1089worst agreement between two runs about the factors, 0 to 1every run fits to 3·10⁻¹³every run fits to 1.2·10⁻¹¹every run factorises to 2.4·10⁻¹⁵ and none agrees with anotherthe only thing that improvestensor, Kruskal holds1tensor, Kruskal fails0.021matrix0.11worst residual1.2·10⁻¹¹three successful fitsone recoverable answer

Every run here reaches its target to the rounding level — 3·10⁻¹³, 1.16·10⁻¹¹ and 2.39·10⁻¹⁵ at worst — so all three are successful factorisations. The bar is the worst agreement between any two of them about the *factors*, matched over permutations and scalings, which is exactly the freedom the uniqueness theorem allows. Kruskal's condition k_A + k_B + k_C ≥ 2r + 2 holds for the first (9 ≥ 8) and fails for the second (6 < 8), and the bars are 1.0000 and 0.0207. The matrix is the comparison the whole thing rests on: AB = (AM)(M⁻¹B) for every invertible M, so its runs agree to 0.1089 and its factors mean nothing on their own.

seeds: 6

The arguments are the ones A factorisation that is unique for once 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.

How much two runs of the same fit agree about the factors, from 6 starting points eachEvery run here reaches its target to the rounding level — 3·10⁻¹³, 1.16·10⁻¹¹ and 2.39·10⁻¹⁵ at worst — so all three are successful factorisations. The bar is the worst agreement between any two of them about the *factors*, matched over permutations and scalings, which is exactly the freedom the uniqueness theorem allows. Kruskal's condition k_A + k_B + k_C ≥ 2r + 2 holds for the first (9 ≥ 8) and fails for the second (6 < 8), and the bars are 1.0000 and 0.0207. The matrix is the comparison the whole thing rests on: AB = (AM)(M⁻¹B) for every invertible M, so its runs agree to 0.1089 and its factors mean nothing on their own.6 × 6 × 6, rank 3 · 9 ≥ 81.00002 × 2 × 2, rank 3 · 6 < 80.02076 × 6 matrix, rank 3 · no condition0.1089worst agreement between two runs about the factors, 0 to 1every run fits to 3·10⁻¹³every run fits to 1.2·10⁻¹¹every run factorises to 2.4·10⁻¹⁵ and none agrees with anotherthe only thing that improvestensor, Kruskal holds1tensor, Kruskal fails0.021matrix0.11worst residual1.2·10⁻¹¹three successful fitsone recoverable answer

Every run here reaches its target to the rounding level — 3·10⁻¹³, 1.16·10⁻¹¹ and 2.39·10⁻¹⁵ at worst — so all three are successful factorisations. The bar is the worst agreement between any two of them about the *factors*, matched over permutations and scalings, which is exactly the freedom the uniqueness theorem allows. Kruskal's condition k_A + k_B + k_C ≥ 2r + 2 holds for the first (9 ≥ 8) and fails for the second (6 < 8), and the bars are 1.0000 and 0.0207. The matrix is the comparison the whole thing rests on: AB = (AM)(M⁻¹B) for every invertible M, so its runs agree to 0.1089 and its factors mean nothing on their own.

seeds: 10

The arguments are the ones A factorisation that is unique for once 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.

How much two runs of the same fit agree about the factors, from 10 starting points eachEvery run here reaches its target to the rounding level — 3·10⁻¹³, 1.16·10⁻¹¹ and 2.39·10⁻¹⁵ at worst — so all three are successful factorisations. The bar is the worst agreement between any two of them about the *factors*, matched over permutations and scalings, which is exactly the freedom the uniqueness theorem allows. Kruskal's condition k_A + k_B + k_C ≥ 2r + 2 holds for the first (9 ≥ 8) and fails for the second (6 < 8), and the bars are 1.0000 and 0.0207. The matrix is the comparison the whole thing rests on: AB = (AM)(M⁻¹B) for every invertible M, so its runs agree to 0.0432 and its factors mean nothing on their own.6 × 6 × 6, rank 3 · 9 ≥ 81.00002 × 2 × 2, rank 3 · 6 < 80.02076 × 6 matrix, rank 3 · no condition0.0432worst agreement between two runs about the factors, 0 to 1every run fits to 3·10⁻¹³every run fits to 1.2·10⁻¹¹every run factorises to 2.4·10⁻¹⁵ and none agrees with anotherthe only thing that improvestensor, Kruskal holds1tensor, Kruskal fails0.021matrix0.043worst residual1.2·10⁻¹¹three successful fitsone recoverable answer

Every run here reaches its target to the rounding level — 3·10⁻¹³, 1.16·10⁻¹¹ and 2.39·10⁻¹⁵ at worst — so all three are successful factorisations. The bar is the worst agreement between any two of them about the *factors*, matched over permutations and scalings, which is exactly the freedom the uniqueness theorem allows. Kruskal's condition k_A + k_B + k_C ≥ 2r + 2 holds for the first (9 ≥ 8) and fails for the second (6 < 8), and the bars are 1.0000 and 0.0207. The matrix is the comparison the whole thing rests on: AB = (AM)(M⁻¹B) for every invertible M, so its runs agree to 0.0432 and its factors mean nothing on their own.

seeds: 3

The arguments are the ones A factorisation that is unique for once 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.

How much two runs of the same fit agree about the factors, from 3 starting points eachEvery run here reaches its target to the rounding level — 1.55·10⁻¹³, 3.59·10⁻¹² and 2.39·10⁻¹⁵ at worst — so all three are successful factorisations. The bar is the worst agreement between any two of them about the *factors*, matched over permutations and scalings, which is exactly the freedom the uniqueness theorem allows. Kruskal's condition k_A + k_B + k_C ≥ 2r + 2 holds for the first (9 ≥ 8) and fails for the second (6 < 8), and the bars are 1.0000 and 0.1450. The matrix is the comparison the whole thing rests on: AB = (AM)(M⁻¹B) for every invertible M, so its runs agree to 0.1089 and its factors mean nothing on their own.6 × 6 × 6, rank 3 · 9 ≥ 81.00002 × 2 × 2, rank 3 · 6 < 80.14506 × 6 matrix, rank 3 · no condition0.1089worst agreement between two runs about the factors, 0 to 1every run fits to 1.6·10⁻¹³every run fits to 3.6·10⁻¹²every run factorises to 2.4·10⁻¹⁵ and none agrees with anotherthe only thing that improvestensor, Kruskal holds1tensor, Kruskal fails0.15matrix0.11worst residual3.6·10⁻¹²three successful fitsone recoverable answer

Every run here reaches its target to the rounding level — 1.55·10⁻¹³, 3.59·10⁻¹² and 2.39·10⁻¹⁵ at worst — so all three are successful factorisations. The bar is the worst agreement between any two of them about the *factors*, matched over permutations and scalings, which is exactly the freedom the uniqueness theorem allows. Kruskal's condition k_A + k_B + k_C ≥ 2r + 2 holds for the first (9 ≥ 8) and fails for the second (6 < 8), and the bars are 1.0000 and 0.1450. The matrix is the comparison the whole thing rests on: AB = (AM)(M⁻¹B) for every invertible M, so its runs agree to 0.1089 and its factors mean nothing on their own.

seeds: 4

The arguments are the ones A factorisation that is unique for once 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.

How much two runs of the same fit agree about the factors, from 4 starting points eachEvery run here reaches its target to the rounding level — 3·10⁻¹³, 3.59·10⁻¹² and 2.39·10⁻¹⁵ at worst — so all three are successful factorisations. The bar is the worst agreement between any two of them about the *factors*, matched over permutations and scalings, which is exactly the freedom the uniqueness theorem allows. Kruskal's condition k_A + k_B + k_C ≥ 2r + 2 holds for the first (9 ≥ 8) and fails for the second (6 < 8), and the bars are 1.0000 and 0.1026. The matrix is the comparison the whole thing rests on: AB = (AM)(M⁻¹B) for every invertible M, so its runs agree to 0.1089 and its factors mean nothing on their own.6 × 6 × 6, rank 3 · 9 ≥ 81.00002 × 2 × 2, rank 3 · 6 < 80.10266 × 6 matrix, rank 3 · no condition0.1089worst agreement between two runs about the factors, 0 to 1every run fits to 3·10⁻¹³every run fits to 3.6·10⁻¹²every run factorises to 2.4·10⁻¹⁵ and none agrees with anotherthe only thing that improvestensor, Kruskal holds1tensor, Kruskal fails0.1matrix0.11worst residual3.6·10⁻¹²three successful fitsone recoverable answer

Every run here reaches its target to the rounding level — 3·10⁻¹³, 3.59·10⁻¹² and 2.39·10⁻¹⁵ at worst — so all three are successful factorisations. The bar is the worst agreement between any two of them about the *factors*, matched over permutations and scalings, which is exactly the freedom the uniqueness theorem allows. Kruskal's condition k_A + k_B + k_C ≥ 2r + 2 holds for the first (9 ≥ 8) and fails for the second (6 < 8), and the bars are 1.0000 and 0.1026. The matrix is the comparison the whole thing rests on: AB = (AM)(M⁻¹B) for every invertible M, so its runs agree to 0.1089 and its factors mean nothing on their own.

seeds: 8

The arguments are the ones A factorisation that is unique for once 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.

How much two runs of the same fit agree about the factors, from 8 starting points eachEvery run here reaches its target to the rounding level — 3·10⁻¹³, 1.16·10⁻¹¹ and 2.39·10⁻¹⁵ at worst — so all three are successful factorisations. The bar is the worst agreement between any two of them about the *factors*, matched over permutations and scalings, which is exactly the freedom the uniqueness theorem allows. Kruskal's condition k_A + k_B + k_C ≥ 2r + 2 holds for the first (9 ≥ 8) and fails for the second (6 < 8), and the bars are 1.0000 and 0.0207. The matrix is the comparison the whole thing rests on: AB = (AM)(M⁻¹B) for every invertible M, so its runs agree to 0.0432 and its factors mean nothing on their own.6 × 6 × 6, rank 3 · 9 ≥ 81.00002 × 2 × 2, rank 3 · 6 < 80.02076 × 6 matrix, rank 3 · no condition0.0432worst agreement between two runs about the factors, 0 to 1every run fits to 3·10⁻¹³every run fits to 1.2·10⁻¹¹every run factorises to 2.4·10⁻¹⁵ and none agrees with anotherthe only thing that improvestensor, Kruskal holds1tensor, Kruskal fails0.021matrix0.043worst residual1.2·10⁻¹¹three successful fitsone recoverable answer

Every run here reaches its target to the rounding level — 3·10⁻¹³, 1.16·10⁻¹¹ and 2.39·10⁻¹⁵ at worst — so all three are successful factorisations. The bar is the worst agreement between any two of them about the *factors*, matched over permutations and scalings, which is exactly the freedom the uniqueness theorem allows. Kruskal's condition k_A + k_B + k_C ≥ 2r + 2 holds for the first (9 ≥ 8) and fails for the second (6 < 8), and the bars are 1.0000 and 0.0207. The matrix is the comparison the whole thing rests on: AB = (AM)(M⁻¹B) for every invertible M, so its runs agree to 0.0432 and its factors mean nothing on their own.

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.

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.

a number of starting points

and only the first is recovered the same way twice

matmul shapes agree

one case satisfies Kruskal's condition and one does not

while every matrix run factorises

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 146 of 287 generators — 131 print a residual and 15 are exempt with a published reason; 141 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.

When the index is a tuple

A factorisation that is unique for once

A rank-r factorisation of a matrix is never unique — AB is (AM)(M⁻¹B) for any invertible M, so no factor means anything on its own. For three indices a checkable condition on the factors' k-ranks makes the decomposition unique up to permuting and scaling the terms, and it holds generically.

When the index is a tuple

A nearest point that is not there

Eckart and Young guarantee that a matrix has a best rank-k approximation and that the truncated SVD is it. For three indices the guarantee is false in the strongest available way — there are tensors whose distance to the rank-two set is zero and which no rank-two tensor equals.

Two errors, and whose fault they are

A tensor that cannot be decomposed

Every member of a certain sequence is exactly a sum of two rank-one terms, and both terms are written down in closed form. A three-hundred-sweep fit from a random start does not find them, and gets further away as the sequence goes on — because the decomposition has a condition number of its own, and it is 2n².

When the index is a tuple

An iteration that walks out of the set

Every sweep of alternating least squares is the exact minimiser of its own subproblem, so the objective can only fall. What it cannot do is converge, when the target's nearest rank-r point is not in the rank-r set — and a plateau at a small residual looks identical to slow convergence unless the size of the terms is plotted beside it.

When the index is a tuple

The orthogonality that cannot be diagonal

A matrix decomposition hands over orthonormal factors and a diagonal middle at once. For three indices the two come apart, and there is no arrangement that has both — so the question stops being which decomposition to use and becomes which of the two properties the computation needs.

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