Row norms are invariant across factorizations (Claim C.2)
ProvedMatrixCompletion.NoSpuriousMin.row_norms_of_factorizationIf satisfy , then every row satisfies ; consequently . In particular all exact factors of are equally incoherent — the symmetric-case substitute for the row-norm bounds on both factors in the asymmetric analysis of Sun–Luo. (Immediate from .)
import Definitions.Def_MCNoSpuriousMinModel open Matrix MatrixCompletion.NoSpuriousMin
theorem MatrixCompletion.NoSpuriousMin.row_norms_of_factorization
{d r : ℕ} (U Z : Matrix (Fin d) (Fin r) ℝ)
(hU : U * Uᵀ = Z * Zᵀ) (i : Fin d) :
rowNorm U i = rowNorm Z i := by sorryRead-back
What the Lean code literally says, in plain math · claude-fable-5
For natural numbers and , given two real matrices and (with rows indexed by and columns by ) satisfying the hypothesis , and given a row index , the theorem asserts that the Euclidean norm of the -th row of equals the Euclidean norm of the -th row of :
where the row norm is the bundle's rowNorm, defined as the square root of the sum of the squares of the row's entries (the square root being the real square root, which returns on negative inputs — irrelevant here since the sum of squares is nonnegative). The matrices and are explicit universally quantified arguments, the dimensions and are implicit universally quantified natural numbers, and the conclusion is stated for the one given index (equivalently, since is universally quantified, for every row index). No rank, incoherence, or nonzero-dimension assumptions are made; the statement includes the degenerate cases (no row indices exist, so the statement is vacuous) and (both row norms are , and the hypothesis holds automatically since both sides are the zero matrix). Note the hypothesis is an exact matrix equality, not an approximation, and the conclusion is an exact equality of real numbers.
Confirmed by the mission captain (proposal self-audit).