CGET22 does an eigenvector check.
 The basic test is:
    RESULT(1) = | A E  -  E W | / ( |A| |E| ulp )
 using the 1-norm.  It also tests the normalization of E:
    RESULT(2) = max | m-norm(E(j)) - 1 | / ( n ulp )
                 j
 where E(j) is the j-th eigenvector, and m-norm is the max-norm of a
 vector.  The max-norm of a complex n-vector x in this case is the
 maximum of |re(x(i)| + |im(x(i)| over i = 1, ..., n.

TRANSA

          TRANSA is CHARACTER*1
          Specifies whether or not A is transposed.
          = 'N':  No transpose
          = 'T':  Transpose
          = 'C':  Conjugate transpose

TRANSE

          TRANSE is CHARACTER*1
          Specifies whether or not E is transposed.
          = 'N':  No transpose, eigenvectors are in columns of E
          = 'T':  Transpose, eigenvectors are in rows of E
          = 'C':  Conjugate transpose, eigenvectors are in rows of E

TRANSW

          TRANSW is CHARACTER*1
          Specifies whether or not W is transposed.
          = 'N':  No transpose
          = 'T':  Transpose, same as TRANSW = 'N'
          = 'C':  Conjugate transpose, use -WI(j) instead of WI(j)

N

          N is INTEGER
          The order of the matrix A.  N >= 0.

A

          A is COMPLEX array, dimension (LDA,N)
          The matrix whose eigenvectors are in E.

LDA

          LDA is INTEGER
          The leading dimension of the array A.  LDA >= max(1,N).

E

          E is COMPLEX array, dimension (LDE,N)
          The matrix of eigenvectors. If TRANSE = 'N', the eigenvectors
          are stored in the columns of E, if TRANSE = 'T' or 'C', the
          eigenvectors are stored in the rows of E.

LDE

          LDE is INTEGER
          The leading dimension of the array E.  LDE >= max(1,N).

W

          W is COMPLEX array, dimension (N)
          The eigenvalues of A.

WORK

          WORK is COMPLEX array, dimension (N*N)

RWORK

          RWORK is REAL array, dimension (N)

RESULT

          RESULT is REAL array, dimension (2)
          RESULT(1) = | A E  -  E W | / ( |A| |E| ulp )
          RESULT(2) = max | m-norm(E(j)) - 1 | / ( n ulp )
                       j

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