Generator

The largest train rank of a 5-index reciprocal tensor on 8 points a side, against the accuracy asked for

One function in the ttrain library, called 7 times across 2 essays. Below: what it draws at its defaults, what it draws at every value an essay asks for, the 10 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 largest train rank of a 5-index reciprocal tensor on 8 points a side, against the accuracy asked for. Every point is a decomposition that met its tolerance: the measured errors are 0.0041, 4.2·10⁻⁴, 3.4·10⁻⁵, 3.3·10⁻⁷, 5.7·10⁻⁹, 6.6·10⁻¹², 1.6·10⁻¹³ against tolerances of 10⁻², 10⁻³, 10⁻⁴, 10⁻⁶, 10⁻⁸, 10⁻¹⁰ and 10⁻¹². The rank runs 3, 4, 5, 7, 8, 10, 11 and the storage 264, 448, 680, 1,160, 1,664, 2,208, 2,504, against 32,768 entries. That is 0.80 of rank per decade of accuracy over the whole range — a constant, with no cliff and no regime where a digit costs more than the last one, which is the same shape the hierarchy field measured for a kernel matrix and is not something either field's geometry promises.

train-accuracy is one function in lib/figures/ttrain.js — the train — one rank per cut of the index list, and the iterate that must be cut back. 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 largest train rank of a 5-index reciprocal tensor on 8 points a side, against the accuracy asked forEvery point is a decomposition that met its tolerance: the measured errors are 0.0041, 4.2·10⁻⁴, 3.4·10⁻⁵, 3.3·10⁻⁷, 5.7·10⁻⁹, 6.6·10⁻¹², 1.6·10⁻¹³ against tolerances of 10⁻², 10⁻³, 10⁻⁴, 10⁻⁶, 10⁻⁸, 10⁻¹⁰ and 10⁻¹². The rank runs 3, 4, 5, 7, 8, 10, 11 and the storage 264, 448, 680, 1,160, 1,664, 2,208, 2,504, against 32,768 entries. That is 0.80 of rank per decade of accuracy over the whole range — a constant, with no cliff and no regime where a digit costs more than the last one, which is the same shape the hierarchy field measured for a kernel matrix and is not something either field's geometry promises.-13-11-9-7-5-3-1024681012log₁₀ of the accuracy asked forlargest train rank0.80 of rank per decadea cost that is typed inentries3.3·10⁴stored at 10⁻²264stored at 10⁻¹²2504rank per decade0.8worst error ⁄ tolerance0.57the storage is chosena constant of rank a decade

Every point is a decomposition that met its tolerance: the measured errors are 0.0041, 4.2·10⁻⁴, 3.4·10⁻⁵, 3.3·10⁻⁷, 5.7·10⁻⁹, 6.6·10⁻¹², 1.6·10⁻¹³ against tolerances of 10⁻², 10⁻³, 10⁻⁴, 10⁻⁶, 10⁻⁸, 10⁻¹⁰ and 10⁻¹². The rank runs 3, 4, 5, 7, 8, 10, 11 and the storage 264, 448, 680, 1,160, 1,664, 2,208, 2,504, against 32,768 entries. That is 0.80 of rank per decade of accuracy over the whole range — a constant, with no cliff and no regime where a digit costs more than the last one, which is the same shape the hierarchy field measured for a kernel matrix and is not something either field's geometry promises.

d: 3, n: 6

The arguments are the ones The digit that costs more than the tensor passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.

The largest train rank of a 3-index reciprocal tensor on 6 points a side, against the accuracy asked forEvery point is a decomposition that met its tolerance: the measured errors are 0.0012, 4.2·10⁻⁵, 4.2·10⁻⁵, 8.3·10⁻⁷, 1.2·10⁻¹⁵, 1.2·10⁻¹⁵, 1.2·10⁻¹⁵ against tolerances of 10⁻², 10⁻³, 10⁻⁴, 10⁻⁶, 10⁻⁸, 10⁻¹⁰ and 10⁻¹². The rank runs 3, 4, 4, 5, 6, 6, 6 and the storage 90, 144, 144, 210, 288, 288, 288, against 216 entries — which the train exceeds at the tight end, so the format stops being a compression before it stops working. That is 0.30 of rank per decade of accuracy over the whole range — a constant, with no cliff and no regime where a digit costs more than the last one, which is the same shape the hierarchy field measured for a kernel matrix and is not something either field's geometry promises.-13-11-9-7-5-3-102468log₁₀ of the accuracy asked forlargest train rank0.30 of rank per decadea cost that is typed inentries216stored at 10⁻²90stored at 10⁻¹²288rank per decade0.3worst error ⁄ tolerance0.83the storage is chosena constant of rank a decade

Every point is a decomposition that met its tolerance: the measured errors are 0.0012, 4.2·10⁻⁵, 4.2·10⁻⁵, 8.3·10⁻⁷, 1.2·10⁻¹⁵, 1.2·10⁻¹⁵, 1.2·10⁻¹⁵ against tolerances of 10⁻², 10⁻³, 10⁻⁴, 10⁻⁶, 10⁻⁸, 10⁻¹⁰ and 10⁻¹². The rank runs 3, 4, 4, 5, 6, 6, 6 and the storage 90, 144, 144, 210, 288, 288, 288, against 216 entries — which the train exceeds at the tight end, so the format stops being a compression before it stops working. That is 0.30 of rank per decade of accuracy over the whole range — a constant, with no cliff and no regime where a digit costs more than the last one, which is the same shape the hierarchy field measured for a kernel matrix and is not something either field's geometry promises.

d: 3

The arguments are the ones The digit that costs more than the tensor passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.

The largest train rank of a 3-index reciprocal tensor on 8 points a side, against the accuracy asked forEvery point is a decomposition that met its tolerance: the measured errors are 0.0023, 1.3·10⁻⁴, 5.1·10⁻⁶, 1.4·10⁻⁷, 2.2·10⁻⁹, 8.6·10⁻¹⁶, 8.6·10⁻¹⁶ against tolerances of 10⁻², 10⁻³, 10⁻⁴, 10⁻⁶, 10⁻⁸, 10⁻¹⁰ and 10⁻¹². The rank runs 3, 4, 5, 6, 7, 8, 8 and the storage 120, 192, 280, 384, 504, 640, 640, against 512 entries — which the train exceeds at the tight end, so the format stops being a compression before it stops working. That is 0.50 of rank per decade of accuracy over the whole range — a constant, with no cliff and no regime where a digit costs more than the last one, which is the same shape the hierarchy field measured for a kernel matrix and is not something either field's geometry promises.-13-11-9-7-5-3-10246810log₁₀ of the accuracy asked forlargest train rank0.50 of rank per decadea cost that is typed inentries512stored at 10⁻²120stored at 10⁻¹²640rank per decade0.5worst error ⁄ tolerance0.23the storage is chosena constant of rank a decade

Every point is a decomposition that met its tolerance: the measured errors are 0.0023, 1.3·10⁻⁴, 5.1·10⁻⁶, 1.4·10⁻⁷, 2.2·10⁻⁹, 8.6·10⁻¹⁶, 8.6·10⁻¹⁶ against tolerances of 10⁻², 10⁻³, 10⁻⁴, 10⁻⁶, 10⁻⁸, 10⁻¹⁰ and 10⁻¹². The rank runs 3, 4, 5, 6, 7, 8, 8 and the storage 120, 192, 280, 384, 504, 640, 640, against 512 entries — which the train exceeds at the tight end, so the format stops being a compression before it stops working. That is 0.50 of rank per decade of accuracy over the whole range — a constant, with no cliff and no regime where a digit costs more than the last one, which is the same shape the hierarchy field measured for a kernel matrix and is not something either field's geometry promises.

d: 3, n: 10

The arguments are the ones The digit that costs more than the tensor passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.

The largest train rank of a 3-index reciprocal tensor on 10 points a side, against the accuracy asked forEvery point is a decomposition that met its tolerance: the measured errors are 0.0034, 2.5·10⁻⁴, 1.4·10⁻⁵, 5.7·10⁻⁷, 4.1·10⁻¹⁰, 5.7·10⁻¹², 1.7·10⁻¹⁵ against tolerances of 10⁻², 10⁻³, 10⁻⁴, 10⁻⁶, 10⁻⁸, 10⁻¹⁰ and 10⁻¹². The rank runs 3, 4, 5, 6, 8, 9, 10 and the storage 150, 240, 350, 480, 800, 990, 1,200, against 1,000 entries — which the train exceeds at the tight end, so the format stops being a compression before it stops working. That is 0.70 of rank per decade of accuracy over the whole range — a constant, with no cliff and no regime where a digit costs more than the last one, which is the same shape the hierarchy field measured for a kernel matrix and is not something either field's geometry promises.-13-11-9-7-5-3-1024681012log₁₀ of the accuracy asked forlargest train rank0.70 of rank per decadea cost that is typed inentries1000stored at 10⁻²150stored at 10⁻¹²1200rank per decade0.7worst error ⁄ tolerance0.57the storage is chosena constant of rank a decade

Every point is a decomposition that met its tolerance: the measured errors are 0.0034, 2.5·10⁻⁴, 1.4·10⁻⁵, 5.7·10⁻⁷, 4.1·10⁻¹⁰, 5.7·10⁻¹², 1.7·10⁻¹⁵ against tolerances of 10⁻², 10⁻³, 10⁻⁴, 10⁻⁶, 10⁻⁸, 10⁻¹⁰ and 10⁻¹². The rank runs 3, 4, 5, 6, 8, 9, 10 and the storage 150, 240, 350, 480, 800, 990, 1,200, against 1,000 entries — which the train exceeds at the tight end, so the format stops being a compression before it stops working. That is 0.70 of rank per decade of accuracy over the whole range — a constant, with no cliff and no regime where a digit costs more than the last one, which is the same shape the hierarchy field measured for a kernel matrix and is not something either field's geometry promises.

d: 3, n: 12

The arguments are the ones The digit that costs more than the tensor passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.

The largest train rank of a 3-index reciprocal tensor on 12 points a side, against the accuracy asked forEvery point is a decomposition that met its tolerance: the measured errors are 0.0045, 3.9·10⁻⁴, 2.7·10⁻⁵, 6.4·10⁻⁸, 2.2·10⁻⁹, 6·10⁻¹¹, 1.5·10⁻¹⁴ against tolerances of 10⁻², 10⁻³, 10⁻⁴, 10⁻⁶, 10⁻⁸, 10⁻¹⁰ and 10⁻¹². The rank runs 3, 4, 5, 7, 8, 9, 11 and the storage 180, 288, 420, 756, 960, 1,188, 1,716, against 1,728 entries. That is 0.80 of rank per decade of accuracy over the whole range — a constant, with no cliff and no regime where a digit costs more than the last one, which is the same shape the hierarchy field measured for a kernel matrix and is not something either field's geometry promises.-13-11-9-7-5-3-1024681012log₁₀ of the accuracy asked forlargest train rank0.80 of rank per decadea cost that is typed inentries1728stored at 10⁻²180stored at 10⁻¹²1716rank per decade0.8worst error ⁄ tolerance0.6the storage is chosena constant of rank a decade

Every point is a decomposition that met its tolerance: the measured errors are 0.0045, 3.9·10⁻⁴, 2.7·10⁻⁵, 6.4·10⁻⁸, 2.2·10⁻⁹, 6·10⁻¹¹, 1.5·10⁻¹⁴ against tolerances of 10⁻², 10⁻³, 10⁻⁴, 10⁻⁶, 10⁻⁸, 10⁻¹⁰ and 10⁻¹². The rank runs 3, 4, 5, 7, 8, 9, 11 and the storage 180, 288, 420, 756, 960, 1,188, 1,716, against 1,728 entries. That is 0.80 of rank per decade of accuracy over the whole range — a constant, with no cliff and no regime where a digit costs more than the last one, which is the same shape the hierarchy field measured for a kernel matrix and is not something either field's geometry promises.

d: 4

The arguments are the ones The digit that costs more than the tensor passes. A value nobody placed would be a picture no essay asked for and no claim was ever checked against.

The largest train rank of a 4-index reciprocal tensor on 8 points a side, against the accuracy asked forEvery point is a decomposition that met its tolerance: the measured errors are 0.0036, 2.9·10⁻⁴, 1.8·10⁻⁵, 2.6·10⁻⁷, 5.4·10⁻⁹, 3.6·10⁻¹¹, 1.4·10⁻¹⁴ against tolerances of 10⁻², 10⁻³, 10⁻⁴, 10⁻⁶, 10⁻⁸, 10⁻¹⁰ and 10⁻¹². The rank runs 3, 4, 5, 7, 8, 9, 11 and the storage 192, 320, 480, 768, 1,008, 1,280, 1,536, against 4,096 entries. That is 0.80 of rank per decade of accuracy over the whole range — a constant, with no cliff and no regime where a digit costs more than the last one, which is the same shape the hierarchy field measured for a kernel matrix and is not something either field's geometry promises.-13-11-9-7-5-3-1024681012log₁₀ of the accuracy asked forlargest train rank0.80 of rank per decadea cost that is typed inentries4096stored at 10⁻²192stored at 10⁻¹²1536rank per decade0.8worst error ⁄ tolerance0.54the storage is chosena constant of rank a decade

Every point is a decomposition that met its tolerance: the measured errors are 0.0036, 2.9·10⁻⁴, 1.8·10⁻⁵, 2.6·10⁻⁷, 5.4·10⁻⁹, 3.6·10⁻¹¹, 1.4·10⁻¹⁴ against tolerances of 10⁻², 10⁻³, 10⁻⁴, 10⁻⁶, 10⁻⁸, 10⁻¹⁰ and 10⁻¹². The rank runs 3, 4, 5, 7, 8, 9, 11 and the storage 192, 320, 480, 768, 1,008, 1,280, 1,536, against 4,096 entries. That is 0.80 of rank per decade of accuracy over the whole range — a constant, with no cliff and no regime where a digit costs more than the last one, which is the same shape the hierarchy field measured for a kernel matrix and is not something either field's geometry promises.

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.

10 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.

the train meets 10^-2 — checked 7 times

a grid the sweep can afford at every tolerance

a number of indices the sweep can afford

and a tighter tolerance costs rank

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 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.

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