tropical-prediction
At its defaults it draws a prediction from three numbers, exact at one end of the spectrum and wrong by n² at the other. The tropical roots of the max-plus quadratic with coefficients ‖M‖, ‖C‖ and ‖K‖, divided by the extreme moduli they are supposed to estimate, for chains of 4 to 32 masses with C = 6M + 0.5K. The large root is within 5.7 per cent of the largest modulus at every size. The small root is out by 5.36, 17.1, 60.8 and 229.6 — a factor growing like the square of the size. The reason is structural: a norm is a maximum, the small end of this spectrum is set by the SMALLEST eigenvalue of K, which is 4sin²(π/2(n+1)), and no norm of K contains that number. A quantity built out of maxima is exact where the answer is a maximum and silent where it is a minimum.
tropical-prediction is one function in lib/figures/qepscale.js —
the units a polynomial is written in — two lines of scaling, a prediction that is half right, and a format's edge. 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 tropical roots of the max-plus quadratic with coefficients ‖M‖, ‖C‖ and ‖K‖, divided by the extreme moduli they are supposed to estimate, for chains of 4 to 32 masses with C = 6M + 0.5K. The large root is within 5.7 per cent of the largest modulus at every size. The small root is out by 5.36, 17.1, 60.8 and 229.6 — a factor growing like the square of the size. The reason is structural: a norm is a maximum, the small end of this spectrum is set by the SMALLEST eigenvalue of K, which is 4sin²(π/2(n+1)), and no norm of K contains that number. A quantity built out of maxima is exact where the answer is a maximum and silent where it is a minimum.
beta: 0.5
The arguments are the ones A spectrum that comes in reciprocal pairs 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.
The tropical roots of the max-plus quadratic with coefficients ‖M‖, ‖C‖ and ‖K‖, divided by the extreme moduli they are supposed to estimate, for chains of 4 to 32 masses with C = 6M + 0.5K. The large root is within 5.7 per cent of the largest modulus at every size. The small root is out by 5.36, 17.1, 60.8 and 229.6 — a factor growing like the square of the size. The reason is structural: a norm is a maximum, the small end of this spectrum is set by the SMALLEST eigenvalue of K, which is 4sin²(π/2(n+1)), and no norm of K contains that number. A quantity built out of maxima is exact where the answer is a maximum and silent where it is a minimum.
beta: 0.2
The arguments are the ones The scaling that buys ten orders 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.
The tropical roots of the max-plus quadratic with coefficients ‖M‖, ‖C‖ and ‖K‖, divided by the extreme moduli they are supposed to estimate, for chains of 4 to 32 masses with C = 6M + 0.2K. The large root is within 4.4 per cent of the largest modulus at every size. The small root is out by 5.76, 18.6, 66.7 and 252 — a factor growing like the square of the size. The reason is structural: a norm is a maximum, the small end of this spectrum is set by the SMALLEST eigenvalue of K, which is 4sin²(π/2(n+1)), and no norm of K contains that number. A quantity built out of maxima is exact where the answer is a maximum and silent where it is a minimum.
beta: 4
The arguments are the ones The scaling that buys ten orders 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.
The tropical roots of the max-plus quadratic with coefficients ‖M‖, ‖C‖ and ‖K‖, divided by the extreme moduli they are supposed to estimate, for chains of 4 to 32 masses with C = 6M + 4K. The large root is within 31 per cent of the largest modulus at every size. The small root is out by 3.09, 8.59, 29 and 107.8 — a factor growing like the square of the size. The reason is structural: a norm is a maximum, the small end of this spectrum is set by the SMALLEST eigenvalue of K, which is 4sin²(π/2(n+1)), and no norm of K contains that number. A quantity built out of maxima is exact where the answer is a maximum and silent where it is a minimum.
beta: 1
The arguments are the ones The scaling that buys ten orders 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.
The tropical roots of the max-plus quadratic with coefficients ‖M‖, ‖C‖ and ‖K‖, divided by the extreme moduli they are supposed to estimate, for chains of 4 to 32 masses with C = 6M + 1K. The large root is within 15 per cent of the largest modulus at every size. The small root is out by 4.8, 15, 52.9 and 199.1 — a factor growing like the square of the size. The reason is structural: a norm is a maximum, the small end of this spectrum is set by the SMALLEST eigenvalue of K, which is 4sin²(π/2(n+1)), and no norm of K contains that number. A quantity built out of maxima is exact where the answer is a maximum and silent where it is a minimum.
beta: 2
The arguments are the ones The scaling that buys ten orders 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.
The tropical roots of the max-plus quadratic with coefficients ‖M‖, ‖C‖ and ‖K‖, divided by the extreme moduli they are supposed to estimate, for chains of 4 to 32 masses with C = 6M + 2K. The large root is within 24 per cent of the largest modulus at every size. The small root is out by 4, 12, 41.6 and 156 — a factor growing like the square of the size. The reason is structural: a norm is a maximum, the small end of this spectrum is set by the SMALLEST eigenvalue of K, which is 4sin²(π/2(n+1)), and no norm of K contains that number. A quantity built out of maxima is exact where the answer is a maximum and silent where it is a minimum.
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.
7 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 chain long enough to have a spectrum and short enough to draw
a positive mass
and it stays within a factor of two and a half of the largest modulus
and the small one drifts
damping that removes energy rather than adding it
damping the tropical roots can be taken of
the large tropical root's ratio does not move with the size
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 173
of 325 generators —
158 print a residual and
15 are exempt with a published reason;
152 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.
A spectrum that comes in reciprocal pairs
A palindromic quadratic reads the same backwards, so λ is an eigenvalue exactly when 1/λ is. A general solver discards that, computes the large half of the spectrum perfectly and the small half to seven digits — and the small half is a division away from being perfect too.
The eigenvalue problem that is not linearThe scaling that buys ten orders
Two lines computed from three norms, a change of variable that is exact in both directions, and the whole of the loss the previous essay measured comes back — flat, at every stop, because after scaling every stop is the same problem.