Linearisation backward error — the series
-
A backward-stable answer to a problem nobody asked
One quadratic eigenvalue problem, in nine systems of units, with a change of variable that is exact in both directions. The residual the solver prints stays at the rounding level at every stop. The answer loses eleven orders of magnitude, and the two facts are consistent.
-
Six routes to one spectrum
Three linearisations of one quadratic, each reduced to a standard eigenvalue problem two ways. All six have exactly the same eigenvalues in exact arithmetic. On a well-scaled problem they differ by noise; on a badly scaled one by a factor of forty; and two of the six are the same matrix.
-
A perturbation that moves every coefficient
The backward error of a polynomial eigenpair is measured against perturbations of all three coefficients at once. Restrict it to the one coefficient anybody is willing to move and the same computed answers are stable at one eigenvalue and unstable at another, by a factor that runs from 1.06 to 6,370 across a single spectrum.
-
The number that moves when the problem does
Two quantities are offered as the condition number of one eigenvalue. One is unmoved to eight digits by a change of variable that is exact in both directions, and grows like the square root of the chain's length. The other is inflated by ten orders by that change of variable, and is ten times too large before anything has been done at all.