The most erasure-robust measurement directions · n = 14
Choose n nonzero vectors in R^d. 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.
Formal definition
- ContainerReal d-space R^d; an answer is n measurement directions
- SubmissionExactly n nonzero vectors with d coordinates each
- ObjectiveMaximize min det(V_S)²/Π|v_i|² over all C(n,d) subsets; determinants and norms are rational, compared by cross-multiplication
- WordingThe normalized volume is a robustness proxy aligned with numerical stability, not the optimal reconstruction error under every noise model; this problem claims maximin volume and nothing more
- ScoringThe record is floor(min normalized volume · 10¹⁸), rounded against the submitter; the page shows that volume, rounded down at the twelfth decimal