Computing <var>s</var> and <var>sep</var>     Next: Error Bounds for Up: Further Details: Error Previous: Balancing and Conditioning

### Computing s and sep

To explain s and sep , we need to introduce  the spectral projector P , and the separation of two matrices  A and B, sep(A , B) .

We may assume the matrix A is in Schur form, because reducing it to this form does not change the values of s and sep. Consider a cluster of m > = 1 eigenvalues, counting multiplicities. Further assume the n-by-n matrix A is where the eigenvalues of the m-by-n matrix are exactly those in which we are interested. In practice, if the eigenvalues on the diagonal of A are in the wrong order, routine xTREXC      can be used to put the desired ones in the upper left corner as shown.

We define the spectral projector, or simply projector P belonging to the eigenvalues of as where R satisfies the system of linear equations Equation (4.3) is called a Sylvester equation . Given the Schur form (4.1), we solve equation (4.3) for R using the subroutine xTRSYL.

We can now define s for the eigenvalues of : In practice we do not use this expression since is hard to compute. Instead we use the more easily computed underestimate which can underestimate the true value of s by no more than a factor . This underestimation makes our error bounds more conservative. This approximation of s is called RCONDE in xGEEVX and xGEESX.

The separation of the matrices and is defined as the smallest singular value of the linear map in (4.3) which takes X to , i.e., This formulation lets us estimate using the condition estimator   xLACON , which estimates the norm of a linear operator given the ability to compute T and quickly for arbitrary x. In our case, multiplying an arbitrary vector by T means solving the Sylvester equation (4.3)  with an arbitrary right hand side using xTRSYL, and multiplying by means solving the same equation with replaced by and replaced by . Solving either equation costs at most operations, or as few as if m << n. Since the true value of sep is but we use , our estimate of sep may differ from the true value by as much as . This approximation to sep is called RCONDV by xGEEVX and xGEESX.

Another formulation which in principle permits an exact evaluation of is where is the Kronecker product of X and Y. This method is generally impractical, however, because the matrix whose smallest singular value we need is m(n - m) dimensional, which can be as large as . Thus we would require as much as extra workspace and operations, much more than the estimation method of the last paragraph.

The expression measures the ``separation'' of the spectra of and in the following sense. It is zero if and only if and have a common eigenvalue, and small if there is a small perturbation of either one that makes them have a common eigenvalue. If and are both Hermitian matrices, then is just the gap, or minimum distance between an eigenvalue of and an eigenvalue of . On the other hand, if and are non-Hermitian, may be much smaller than this gap.     Next: Error Bounds for Up: Further Details: Error Previous: Balancing and Conditioning

Tue Nov 29 14:03:33 EST 1994