The largest train rank of a 5-index reciprocal tensor on 8 points a side, against the accuracy asked 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.
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.
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.
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.
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.
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.
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.
The digit that costs more than the tensor
Ask a three-index reciprocal tensor on six points a side for seven digits and its train is 288 numbers against 216 entries. The break-even rank is n − 1 at all four grids measured, and a train that reaches it fits with exactly n numbers to spare.
When the index is a tupleThe format that does not notice the dimension
A Tucker core is r^d numbers, so the format that repaired the definition still cannot go past five indices. Cutting between the indices rather than across them gives d − 1 ranks instead of d, storage linear in the number of indices, and a family whose ranks are two everywhere by an addition formula.