Rank-revealing QR — where it appears
Named by 3 essays across one field — each of them below, with the objects they name alongside it.
Rank is a decision
A floating-point matrix does not have a rank. It has a spectrum of singular values, and somewhere in that spectrum is a place where the values stop being signal and start being noise. Deciding where is a judgement, and the evidence for it is a gap.
The cheap rank and what it cannot see
Almost nobody computes singular values to decide a rank. The standard substitute is QR with column pivoting, read off the diagonal of R — and there is a triangular matrix on which the greedy rule makes no interchange at all, has no better column available at any step, and reports a matrix eight orders of magnitude further from singular than it is.
A good curve and a bad verdict
The diagonal of a column-pivoted R is famous for the one matrix it is wrong about. On that matrix it is right about thirty-nine of its forty entries — every |rₖₖ| within a factor of six of the σₖ it stands for — and wrong by 4·10⁶ at the fortieth, which is the only one a rank verdict ever reads.
Named alongside it
The objects these essays reach for when they reach for this one.
Column pivotingNumerical rankSingular valuesCounterexampleKahan's matrixToleranceCondition-estimationCondition numberEckart–YoungHouseholder reflectionJacobi's eigenvalue methodLow-rank approximation