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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