Math Mapgenerated 2026-10-25 · a354234
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run 025Settled

Does positivity give only bounds, or does it sometimes give values?

slug
delsarte-lp-tightness
state
run
created
2026-09-13
settles
resolves a tension in the bounding-mechanism table
script
delsarte_lp.py
outcome
confirmed
source
../lab/runs/025-delsarte-lp-tightness.md

PredictionThe LP bound is tight in the exceptional dimensions and loose elsewhere, so positivity gives a value only when the bound happens to be attained

Falsifierthe bound tight in generic dimensions too, or loose in d=8 where the kissing number is proved

Verdictconfirmed - and it makes the table's claim precise rather than merely correct

Confidence0.85

The tension. The bounding-mechanism table says positivity gives inequalities and never values. But the Tsirelson run produced exactly 2√2 — a value. Either the table is wrong or "bound" and "value" coincide under some condition, and the condition was not stated.

The test. Delsarte's linear-programming bound for kissing numbers: optimise over Gegenbauer coefficients subject to a sign condition on the forbidden arc. Finite-dimensional, and the true answer is known in several dimensions, so correctness is checkable.

dLP boundtrueratio
26.00061.0000 tight
313.158121.0965 loose
425.558241.0649 loose
546.338401.1584 loose
8239.9992401.0000 tight

Resolution. Positivity does not produce values. It produces bounds that sometimes coincide with values, and the coincidence is a property of the dimension, not of the method. Tight at d = 2 and d = 8 — and, in the literature, at d = 24 — loose by 6–16% everywhere between. The same exceptional pair where the sphere-packing proofs go through.

And two of this project's mechanisms meet on one number. 240 is reachable as a count (dim M₄(SL₂(ℤ)) = 1 forcing the theta series) and as an attained positivity bound (an LP over Gegenbauer coefficients). Two independent instruments, one answer, in the one dimension where both work. First instance of that convergence.

Two methodological notes. The LP was written backwards first — maximising where it should minimise — and returned inf rather than a plausible wrong number, so the error announced itself. And SCS ran five minutes without converging on a problem HiGHS solved in under a second: it is a linear program, and it was handed to a conic solver.

generated 2026-10-25the lab index