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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