Eigenvectors — where it appears
Named by 2 essays across 2 fields — each of them below, with the objects they name alongside it.
The basis decides what a filter is
The vocabulary of regularisation is spectral — a method keeps a component or discards it, and the weights are a function of the singular value. Row-normalising a symmetric blur so that it preserves a constant makes it 8.6% asymmetric, and that is enough to move GMRES's weights from 7·10⁻¹⁴ off a function of σ to 4.4·10⁻².
A function of a matrix is not a function of its entries
Everybody learns that f(A) means diagonalise, apply f to the eigenvalues, undiagonalise. That is a definition, not a method. On a matrix seven picometres from a defective one — with exact eigenvalues and eigenvectors from a closed form — the definition returns an answer wrong by sixty-five orders of magnitude, and a method that never mentions an eigenvalue returns the right one.
Named alongside it
The objects these essays reach for when they reach for this one.
Arnoldi iterationCondition numberDefective matrixEigenvaluesExact ground truthFilter factorsGMRESIll posed problemJordan formKrylov subspaceLsqrMatrix exponential