Further Details: Error Bounds for the Singular Value Decomposition

The usual error analysis of the SVD algorithms xGESVD and xGESDD in LAPACK (see subsection 2.3.4) or the routines in LINPACK and EISPACK is as follows [25,55]:

The SVD algorithm is backward stable. This means that the computed SVD, , is nearly the exact SVD ofwhere , andA+Eis a modestly growing function ofp(m,n)andm. This means is the true SVD, so that and are both orthogonal, where , and . Each computed singular value differs from true by at mostn

(we takein the code fragment). Thus large singular values (those near ) are computed to high relative accuracy and small ones may not be.p(m,n)=1

There are two questions to ask about the computed singular vectors: ``Are they orthogonal?'' and ``How much do they differ from the true eigenvectors?'' The answer to the first question is yes, the computed singular vectors are always nearly orthogonal to working precision, independent of how much they differ from the true singular vectors. In other words

for .

Here is the answer to the second question about singular vectors. The angular difference between the computed left singular vector and a truesatisfies the approximate boundu_{i}

where is theabsolute gapbetween and the nearest other singular value. We takein the code fragment. Thus, if is close to other singular values, its corresponding singular vectorp(m,n)=1may be inaccurate. Whenu_{i}, then must be redefined as . The gaps may be easily computed from the array of computed singular values using functionn<mSDISNA. The gaps computed bySDISNAare ensured not to be so small as to cause overflow when used as divisors. The same bound applies to the computed right singular vector and a true vector.v_{i}

Let be the space spanned by a collection of computed left singular vectors , where is a subset of the integers from 1 to. Let be the corresponding true space. Thenn

where

is the absolute gap between the singular values in and the nearest other singular value. Thus, a cluster of close singular values which is far away from any other singular value may have a well determined space even if its individual singular vectors are ill-conditioned. The same bound applies to a set of right singular vectors^{4.1}.

In the special case of bidiagonal matrices, the singular values and
singular vectors may be computed much more accurately. A bidiagonal
matrix ** B** has nonzero entries only on the main diagonal and the diagonal
immediately
above it (or immediately below it). xGESVD computes the SVD of a general
matrix by first reducing it to bidiagonal form

Each computed singular value of a bidiagonal matrix is accurate to nearly full relative accuracy, no matter how tiny it is:

The following bounds apply only to xBDSQR. The computed left singular vector has an angular error at most about

where is therelative gapbetween and the nearest other singular value. The same bound applies to the right singular vector and. Since the relative gap may be much larger than the absolute gap, this error bound may be much smaller than the previous one. The relative gaps may be easily computed from the array of computed singular values.v_{i}

In the very special case of 2-by-2 bidiagonal matrices, xBDSQR and xBDSDC call auxiliary routine xLASV2 to compute the SVD. xLASV2 will actually compute nearly correctly rounded singular vectors independent of the relative gap, but this requires accurate computer arithmetic: if leading digits cancel during floating-point subtraction, the resulting difference must be exact. On machines without guard digits one has the slightly weaker result that the algorithm is componentwise relatively backward stable, and therefore the accuracy of the singular vectors depends on the relative gap as described above.

Jacobi's method [34,99,91] is another algorithm for finding singular values and singular vectors of matrices. It is slower than the algorithms based on first bidiagonalizing the matrix, but is capable of computing more accurate answers in several important cases.