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Classical column-pivoted QR can fail the strong coefficient bound
Statement refuted
Every exact classical column-pivoted QR output satisfies the strong rank-revealing coefficient bound for every target rank .
Facts & Assumptions
Given: The real matrix defined below, greedy residual-norm column pivoting, tie-breaking by the smallest original column index, and target rank .
Full, reduced, and column-pivoted computational QR factorisations: a CPQR factorization permutes columns and has orthonormal with upper trapezoidal .
The exact-arithmetic success condition in Strong rank-revealing QR with finite swaps and explicit statuses with factor gives for every entry; this counterexample compares exact factors.
Exact column-pivoted QR of a real or complex rank-r matrix has an invertible leading triangular block and zero trailing block: column pivoting chooses a remaining column with maximal residual norm at each step; the statement applies to rank here.
Equal residual norms are resolved by the smallest original column index, as specified for this example.
Counterexample
Let , , and define
Column of has entries for , for , and zero below. Its squared norm is , using . After the first columns have been selected, they span the first coordinate vectors because their leading triangular block has nonzero diagonal. The residual of each remaining column has squared norm (the sum is empty when ). Thus all remaining residuals tie at every step, and the declared tie rule makes CPQR select columns in their original order.
By [F1] and step 1.1, the resulting permutation is the identity. The matrix is upper triangular with positive diagonal, so the QR factors can be taken as and . For , solving gives , , and . Hence , violating the coefficient condition in [F2].
This CPQR output therefore fails the strong coefficient test with . It does not contradict the strong RRQR procedure: that procedure performs additional swaps when its coefficient or combined residual score exceeds the threshold.
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