!>
!> SSVDCH checks to see if SVD(1) ,..., SVD(N) are accurate singular
!> values of the bidiagonal matrix B with diagonal entries
!> S(1) ,..., S(N) and superdiagonal entries E(1) ,..., E(N-1)).
!> It does this by expanding each SVD(I) into an interval
!> [SVD(I) * (1-EPS) , SVD(I) * (1+EPS)], merging overlapping intervals
!> if any, and using Sturm sequences to count and verify whether each
!> resulting interval has the correct number of singular values (using
!> SSVDCT). Here EPS=TOL*MAX(N/10,1)*MACHEP, where MACHEP is the
!> machine precision. The routine assumes the singular values are sorted
!> with SVD(1) the largest and SVD(N) smallest.  If each interval
!> contains the correct number of singular values, INFO = 0 is returned,
!> otherwise INFO is the index of the first singular value in the first
!> bad interval.
!> 

N

!>          N is INTEGER
!>          The dimension of the bidiagonal matrix B.
!> 

S

!>          S is REAL array, dimension (N)
!>          The diagonal entries of the bidiagonal matrix B.
!> 

E

!>          E is REAL array, dimension (N-1)
!>          The superdiagonal entries of the bidiagonal matrix B.
!> 

SVD

!>          SVD is REAL array, dimension (N)
!>          The computed singular values to be checked.
!> 

TOL

!>          TOL is REAL
!>          Error tolerance for checking, a multiplier of the
!>          machine precision.
!> 

INFO

!>          INFO is INTEGER
!>          =0 if the singular values are all correct (to within
!>             1 +- TOL*MACHEPS)
!>          >0 if the interval containing the INFO-th singular value
!>             contains the incorrect number of singular values.
!> 

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