Bidiagonal matrix — where it appears
Named by 2 essays across one field — each of them below, with the objects they name alongside it.
Also named here as graded matrix, relative accuracy — the same set of essays touches all of them, so they are one junction rather than several.
Small compared to what
This site's own singular value routine has carried a sentence since the month it was written — that one-sided Jacobi computes the small singular values to high relative accuracy and the standard method does not. It has never been measured here, because measuring it needs a σ that is known rather than computed. A bidiagonal matrix and a Sturm count in exact rationals supply one.
Accurate is not a property of a method
A bidiagonal matrix whose every entry is 1 or 4096 has singular values spanning thirty decades. On it, the method recommended for small singular values loses the small one by one and a half per cent, the sweep with the theorem behind it does not converge at all, and the shift the theorem is a warning about gets every value to 5·10⁻¹⁶. Nothing there contradicts the theory.
Named alongside it
The objects these essays reach for when they reach for this one.
Exact arithmeticGraded matrixJacobi's eigenvalue methodRelative accuracySingular valuesCancellationCondition numberCondition squaringConvergence rateEquilibrationSturm sequence