Convergence criterion for a stationary iteration
ID: convergence-criterion-for-a-stationary-iteration
A stationary iterative method for a linear system converges to its specified fixed point for every initial vector if and only if its iteration matrix satisfies , where the spectral radius is the largest modulus of an eigenvalue.[ref]
Notes for Ma221 Lecture 21, Nov 10 2009. Math 221, Matrix Computations, Fall 2009; Basic iterative methods: theorem following the spectral-radius lemmas, before the Jacobi update
For a diagonalizable matrix, write . Then , so each eigenvector coefficient tends to zero exactly when its eigenvalue has modulus less than one. For a general matrix, the Jordan normal form gives the same result: a Jordan block with a nilpotent matrix of size satisfiesIf , every coefficient tends to zero because exponential decay beats any fixed polynomial in ; if , the block vanishes once . Conversely, an eigenvector with provides an initial error that cannot tend to zero. For a real matrix with complex eigenvalues, the same obstruction appears in the real invariant subspace spanned by the real and imaginary parts of an eigenvector.
The words "every initial vector" matter. For example, fails the criterion, but still tends to zero. Here a generic initial error has a nonzero first coordinate and diverges; the exceptional errors lie on the second coordinate axis. With , generic errors oscillate rather than diverge in norm, while the same exceptional axis still converges. Both are valid matrix splittings with and . In general the convergent errors form the direct sum of the generalized eigenspaces for ; if , this is a proper linear subspace.
A small spectral radius describes eventual convergence, but does not imply that the Euclidean norm of the error decreases on every step for a non-normal matrix. An operator norm bound does give the stepwise estimate . For a real symmetric matrix, the matrix 2-norm equals its spectral radius, making this particularly direct.
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