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If we tried to compute the trivial eigenvalues in the same way as the nontrivial ones, that is by taking ratios of the leading diagonal entries of and , we would get 0/0. For a detailed mathematical discussion of this decomposition, see the discussion of the Kronecker Canonical Form in [43].

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If , we may add some zero rows to to make it upper triangular.

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This is the case on Cybers, Cray X-MP, Cray Y-MP, Cray 2 and Cray C90.

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See subsection 2.1.3 for explanation of the naming convention used for LAPACK routines.

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Important machines that do not implement the IEEE standard include the Cray XMP, Cray YMP, Cray 2, Cray C90, IBM 370 and DEC Vax. Some architectures have two (or more) modes, one that implements IEEE arithmetic, and another that is less accurate but faster.

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Machines implementing IEEE arithmetic can continue to compute past overflows, and even division by zero, square roots of negative numbers, etc., by producing infinity and NaN (``Not a Number'') symbols. These are special floating-point numbers subject to special rules of arithmetic. The default on many systems is to continue computing with these symbols, rather than giving an error message, which would often be more convenient for debugging. It is also possible to stop with an error message. The user should consult the system manual to see how to turn error messages on or off.                    

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Sometimes our algorithms satisfy only where both and are small. This does not significantly change the following analysis.

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More generally, we only need Lipschitz continuity of , and may use the Lipschitz constant in place of in deriving error bounds.

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This is a different use of the term ill-posed than used in other contexts. For example, to be well-posed (not ill-posed) in the sense of Hadamard, it is sufficient for to be continuous, whereas we require Lipschitz continuity.

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There are some caveats to this statement. When computing the inverse of a matrix, the backward error is small taking the columns of the computed inverse one at a time, with a different for each column [38]. The same is true when computing the eigenvectors of a nonsymmetric matrix. When computing the eigenvalues and eigenvectors of , or , with symmetric and symmetric and positive definite (using xSYGV or xHEGV) then the method may not be backward normwise stable if      has a large condition number , although it has useful error bounds in this case too (see section 4.10). Solving the Sylvester equation  for the matrix may not be backward stable, although there are again useful error bounds for [54].

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For other algorithms, the answers (and computed error bounds) are as accurate as though the algorithms were componentwise relatively backward stable, even though they are not. These algorithms are called componentwise relatively forward stable.

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As discussed in section 4.2, this approximate error bound may underestimate the true error by a factor which is a modestly growing function of the problem dimension . Often .

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This and other numerical examples were computed in IEEE single precision arithmetic [4] on a DEC 5000/120 workstation.

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These bounds are special cases of those in section 4.8.

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Although such a one-to-one correspondence between computed and true eigenvalues exists, it is not as simple to describe as in the symmetric case. In the symmetric case the eigenvalues are real and simply sorting provides the one-to-one correspondence, so that and . With nonsymmetric matrices is usually just the computed eigenvalue closest to , but in very ill-conditioned problems this is not always true. In the most general case, the one-to-one correspondence may be described in the following nonconstructive way: Let be the eigenvalues of and be the eigenvalues of . Let be the eigenvalues of , where is a parameter, which is initially zero, so that we may set . As increase from 0 to 1, traces out a curve from to , providing the correspondence. Care must be taken when the curves intersect, and the correspondence may not be unique.

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These bounds are special cases of those in sections 4.7 and 4.8, since the singular values and vectors of are simply related to the eigenvalues and eigenvectors of the Hermitian matrix [p. 427]GVL2.

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This bound is guaranteed only if the Level 3 BLAS are implemented in a conventional way, not in a fast way as described in section 4.13.

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Another interpretation of chordal distance is as half the usual Euclidean distance between the projections of and on the Riemann sphere, i.e., half the length of the chord connecting the projections.

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(Input or output) means that the argument may be either an input argument or an output argument, depending on the values of other arguments; for example, in the xyySVX driver routines, some arguments are used either as output arguments to return details of a factorization, or as input arguments to supply details of a previously computed factorization.

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(Workspace/output) means that the argument is used principally as a work array, but may also return some useful information (in its first element)

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Changing DBLE to DREAL must be selective, because instances of DBLE with an integer argument must not be changed. The compiler should flag any instances of DBLE with a COMPLEX*16 argument if it does not accept them.

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The requirement is stated ``LDA max(1,N)'' rather than simply ``LDA N'' because LDA must always be at least 1, even if N = 0, to satisfy the requirements of standard Fortran; on some systems, a zero or negative value of LDA would cause a run-time fault.