Generator

swamp-trace

One function in the cpals library, called 13 times across 6 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 alternating least squares on a tensor with a rank-two answer and on one without, over 20,000 sweeps. The steeply falling curve is a rank-three fit to a tensor built from rank-three factors: it reaches 9.74·10⁻¹⁵ in 58 sweeps and its largest term does not move — 12.202 at sweep twenty and 12.202 at the end. The other is a rank-two fit to the border-rank tensor, whose error goes from 0.00677 to 0.00141 and has not finished, while the rising curve is its largest term: 4.698 to 10.15, a factor of 2.16. Fitted over 199 points, the error falls as the 2.03 power of the term size — a better path than the explicit sequence's 1/n, and still one that leaves the set rather than converging inside it.

swamp-trace 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.

Alternating least squares on a tensor with a rank-two answer and on one without, over 20,000 sweepsThe steeply falling curve is a rank-three fit to a tensor built from rank-three factors: it reaches 9.74·10⁻¹⁵ in 58 sweeps and its largest term does not move — 12.202 at sweep twenty and 12.202 at the end. The other is a rank-two fit to the border-rank tensor, whose error goes from 0.00677 to 0.00141 and has not finished, while the rising curve is its largest term: 4.698 to 10.15, a factor of 2.16. Fitted over 199 points, the error falls as the 2.03 power of the term size — a better path than the explicit sequence's 1/n, and still one that leaves the set rather than converging inside it.110¹10²10³10⁴10⁻¹⁴10⁻¹¹10⁻⁸10⁻⁵10⁻²10¹sweeprelative error, and the largest term's sizerising: the swamp's largest rank-one termfalling, slowly: its errorfalling, once: a fit with an answera plateau with a rising floorswamp error0.0014swamp term10term growth2.2benign error9.7·10⁻¹⁵benign term growth1the error alone cannot tellthe size of the terms can

The steeply falling curve is a rank-three fit to a tensor built from rank-three factors: it reaches 9.74·10⁻¹⁵ in 58 sweeps and its largest term does not move — 12.202 at sweep twenty and 12.202 at the end. The other is a rank-two fit to the border-rank tensor, whose error goes from 0.00677 to 0.00141 and has not finished, while the rising curve is its largest term: 4.698 to 10.15, a factor of 2.16. Fitted over 199 points, the error falls as the 2.03 power of the term size — a better path than the explicit sequence's 1/n, and still one that leaves the set rather than converging inside it.

sweeps: 20000

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.

Alternating least squares on a tensor with a rank-two answer and on one without, over 20,000 sweepsThe steeply falling curve is a rank-three fit to a tensor built from rank-three factors: it reaches 9.74·10⁻¹⁵ in 58 sweeps and its largest term does not move — 12.202 at sweep twenty and 12.202 at the end. The other is a rank-two fit to the border-rank tensor, whose error goes from 0.00677 to 0.00141 and has not finished, while the rising curve is its largest term: 4.698 to 10.15, a factor of 2.16. Fitted over 199 points, the error falls as the 2.03 power of the term size — a better path than the explicit sequence's 1/n, and still one that leaves the set rather than converging inside it.110¹10²10³10⁴10⁻¹⁴10⁻¹¹10⁻⁸10⁻⁵10⁻²10¹sweeprelative error, and the largest term's sizerising: the swamp's largest rank-one termfalling, slowly: its errorfalling, once: a fit with an answera plateau with a rising floorswamp error0.0014swamp term10term growth2.2benign error9.7·10⁻¹⁵benign term growth1the error alone cannot tellthe size of the terms can

The steeply falling curve is a rank-three fit to a tensor built from rank-three factors: it reaches 9.74·10⁻¹⁵ in 58 sweeps and its largest term does not move — 12.202 at sweep twenty and 12.202 at the end. The other is a rank-two fit to the border-rank tensor, whose error goes from 0.00677 to 0.00141 and has not finished, while the rising curve is its largest term: 4.698 to 10.15, a factor of 2.16. Fitted over 199 points, the error falls as the 2.03 power of the term size — a better path than the explicit sequence's 1/n, and still one that leaves the set rather than converging inside it.

sweeps: 200

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.

Alternating least squares on a tensor with a rank-two answer and on one without, over 200 sweepsThe steeply falling curve is a rank-three fit to a tensor built from rank-three factors: it reaches 9.74·10⁻¹⁵ in 58 sweeps and its largest term does not move — 12.202 at sweep twenty and 12.202 at the end. The other is a rank-two fit to the border-rank tensor, whose error goes from 0.00677 to 0.00616 and has not finished, while the rising curve is its largest term: 4.698 to 4.918, a factor of 1.05. Fitted over 149 points, the error falls as the 2.07 power of the term size — a better path than the explicit sequence's 1/n, and still one that leaves the set rather than converging inside it.110¹10²10⁻¹⁴10⁻¹¹10⁻⁸10⁻⁵10⁻²10¹sweeprelative error, and the largest term's sizerising: the swamp's largest rank-one termfalling, slowly: its errorfalling, once: a fit with an answera plateau with a rising floorswamp error0.0062swamp term4.9term growth1benign error9.7·10⁻¹⁵benign term growth1the error alone cannot tellthe size of the terms can

The steeply falling curve is a rank-three fit to a tensor built from rank-three factors: it reaches 9.74·10⁻¹⁵ in 58 sweeps and its largest term does not move — 12.202 at sweep twenty and 12.202 at the end. The other is a rank-two fit to the border-rank tensor, whose error goes from 0.00677 to 0.00616 and has not finished, while the rising curve is its largest term: 4.698 to 4.918, a factor of 1.05. Fitted over 149 points, the error falls as the 2.07 power of the term size — a better path than the explicit sequence's 1/n, and still one that leaves the set rather than converging inside it.

sweeps: 5000

The arguments are the ones A nearest point that is not there 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.

Alternating least squares on a tensor with a rank-two answer and on one without, over 5,000 sweepsThe steeply falling curve is a rank-three fit to a tensor built from rank-three factors: it reaches 9.74·10⁻¹⁵ in 58 sweeps and its largest term does not move — 12.202 at sweep twenty and 12.202 at the end. The other is a rank-two fit to the border-rank tensor, whose error goes from 0.00677 to 0.00266 and has not finished, while the rising curve is its largest term: 4.698 to 7.42, a factor of 1.58. Fitted over 197 points, the error falls as the 2.04 power of the term size — a better path than the explicit sequence's 1/n, and still one that leaves the set rather than converging inside it.110¹10²10³10⁻¹⁴10⁻¹¹10⁻⁸10⁻⁵10⁻²10¹sweeprelative error, and the largest term's sizerising: the swamp's largest rank-one termfalling, slowly: its errorfalling, once: a fit with an answera plateau with a rising floorswamp error0.0027swamp term7.4term growth1.6benign error9.7·10⁻¹⁵benign term growth1the error alone cannot tellthe size of the terms can

The steeply falling curve is a rank-three fit to a tensor built from rank-three factors: it reaches 9.74·10⁻¹⁵ in 58 sweeps and its largest term does not move — 12.202 at sweep twenty and 12.202 at the end. The other is a rank-two fit to the border-rank tensor, whose error goes from 0.00677 to 0.00266 and has not finished, while the rising curve is its largest term: 4.698 to 7.42, a factor of 1.58. Fitted over 197 points, the error falls as the 2.04 power of the term size — a better path than the explicit sequence's 1/n, and still one that leaves the set rather than converging inside it.

sweeps: 1000

The arguments are the ones An iteration that walks out of the set 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.

Alternating least squares on a tensor with a rank-two answer and on one without, over 1,000 sweepsThe steeply falling curve is a rank-three fit to a tensor built from rank-three factors: it reaches 9.74·10⁻¹⁵ in 58 sweeps and its largest term does not move — 12.202 at sweep twenty and 12.202 at the end. The other is a rank-two fit to the border-rank tensor, whose error goes from 0.00677 to 0.00469 and has not finished, while the rising curve is its largest term: 4.698 to 5.618, a factor of 1.20. Fitted over 189 points, the error falls as the 2.06 power of the term size — a better path than the explicit sequence's 1/n, and still one that leaves the set rather than converging inside it.110¹10²10³10⁻¹⁴10⁻¹¹10⁻⁸10⁻⁵10⁻²10¹sweeprelative error, and the largest term's sizerising: the swamp's largest rank-one termfalling, slowly: its errorfalling, once: a fit with an answera plateau with a rising floorswamp error0.0047swamp term5.6term growth1.2benign error9.7·10⁻¹⁵benign term growth1the error alone cannot tellthe size of the terms can

The steeply falling curve is a rank-three fit to a tensor built from rank-three factors: it reaches 9.74·10⁻¹⁵ in 58 sweeps and its largest term does not move — 12.202 at sweep twenty and 12.202 at the end. The other is a rank-two fit to the border-rank tensor, whose error goes from 0.00677 to 0.00469 and has not finished, while the rising curve is its largest term: 4.698 to 5.618, a factor of 1.20. Fitted over 189 points, the error falls as the 2.06 power of the term size — a better path than the explicit sequence's 1/n, and still one that leaves the set rather than converging inside it.

sweeps: 40000

The arguments are the ones An iteration that walks out of the set 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.

Alternating least squares on a tensor with a rank-two answer and on one without, over 40,000 sweepsThe steeply falling curve is a rank-three fit to a tensor built from rank-three factors: it reaches 9.74·10⁻¹⁵ in 58 sweeps and its largest term does not move — 12.202 at sweep twenty and 12.202 at the end. The other is a rank-two fit to the border-rank tensor, whose error goes from 0.00677 to 0.00101 and has not finished, while the rising curve is its largest term: 4.698 to 11.99, a factor of 2.55. Fitted over 199 points, the error falls as the 2.02 power of the term size — a better path than the explicit sequence's 1/n, and still one that leaves the set rather than converging inside it.110¹10²10³10⁴10⁻¹⁴10⁻¹¹10⁻⁸10⁻⁵10⁻²10¹sweeprelative error, and the largest term's sizerising: the swamp's largest rank-one termfalling, slowly: its errorfalling, once: a fit with an answera plateau with a rising floorswamp error0.001swamp term12term growth2.6benign error9.7·10⁻¹⁵benign term growth1the error alone cannot tellthe size of the terms can

The steeply falling curve is a rank-three fit to a tensor built from rank-three factors: it reaches 9.74·10⁻¹⁵ in 58 sweeps and its largest term does not move — 12.202 at sweep twenty and 12.202 at the end. The other is a rank-two fit to the border-rank tensor, whose error goes from 0.00677 to 0.00101 and has not finished, while the rising curve is its largest term: 4.698 to 11.99, a factor of 2.55. Fitted over 199 points, the error falls as the 2.02 power of the term size — a better path than the explicit sequence's 1/n, and still one that leaves the set rather than converging inside it.

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 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 run long enough to see the plateau

and the benign run's terms do not move

matmul shapes agree

the swamp's error keeps falling

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.

When the index is a tuple

A rank that is not a property of the tensor

The same eight real numbers have rank three over the reals and rank two over the complexes, and a random 2 × 2 × 2 tensor has rank two with probability exactly π/4. Neither sentence has an analogue for matrices, where the rank is one number and a random matrix has the largest one.

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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