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# Error Bounds for the Singular Value Decomposition

The singular  value decomposition (SVD) of a real m-by-n matrix A is defined as follows. Let . The SVD of A is ( in the complex case), where U and V are orthogonal (unitary) matrices and is diagonal, with . The are the singular values of A and the leading r columns of U and of V the left and right singular vectors, respectively. The SVD of a general matrix is computed by PxGESVD      (see subsection 3.2.3).

The approximate error bounds for the computed singular values are The approximate error bounds for the computed singular vectors and , which bound the acute angles between the computed singular vectors and true singular vectors and , are These bounds can be computing by the following code fragment:

```      EPSMCH = PSLAMCH( ICTXT, 'E' )
*     Compute singular value decomposition of A
*     The singular values are returned in S
*     The left singular vectors are returned in U
*     The transposed right singular vectors are returned in VT
CALL PSGESVD( 'V', 'V', M, N, A, IA, JA, DESCA, S, U, IU, JU,
\$              DESCU, VT, IVT, JVT, DESCVT, WORK, LWORK, INFO )
IF( INFO.GT.0 ) THEN
PRINT *,'PSGESVD did not converge'
ELSE IF( MIN( M, N ).GT.0 ) THEN
SERRBD  = EPSMCH * S( 1 )
*        Compute reciprocal condition numbers for singular vectors
CALL SDISNA( 'Left', M, N, S, RCONDU, INFO )
CALL SDISNA( 'Right', M, N, S, RCONDV, INFO )
DO 10 I = 1, MIN( M, N )
VERRBD( I ) = EPSMCH*( S( 1 ) / RCONDV( I ) )
UERRBD( I ) = EPSMCH*( S( 1 ) / RCONDU( I ) )
10       CONTINUE
END IF```

For example, if and then the singular values, approximate error bounds, and true errors are given below. Susan Blackford
Tue May 13 09:21:01 EDT 1997