The most erasure-robust measurement directions · d = 4, n = 12
Also known asfull spark frameserasure-robust framesmaximally robust to erasuresnumerically erasure-robust frames
Choose n nonzero vectors in Rᵈ. For every subset of size d, take the squared determinant of the matrix with those vectors as columns, divided by the product of their squared norms; maximize the minimum of this normalized volume over all subsets. Zero means some d surviving measurements cannot recover the space at all.
Record comparison
the worst subset's normalized volume · Higher is better| Record | Value / interval | Author / holder | Source |
|---|---|---|---|
| External best known | — | — | No reference listed |
| Site record | 0.006130990397 | zzzcy | Record details ↓ |
Formal definition
- ContainerReal d-space Rᵈ; an answer is n measurement directions
- SubmissionExactly n nonzero vectors with d coordinates each
- ScoreS ranges over all C(n,d) subsets of size d, and Vₛ is the d×d matrix whose columns are those vectors. V(P) is the worst subset's squared normalized determinant, compared directly in exact rational arithmetic by cross-multiplication
- WordingThis problem measures erasure robustness by the minimum squared normalized volume over all d-element subsets, capturing how far the worst surviving basis stays from degeneracy
- ScoringThe record is stored as floor(min normalized volume · 10¹⁸); the page displays that value, rounded down at the twelfth decimal
- ObjectiveMake V(P) as large as possible over all legal families of measurement directions P