Note: the dimension of the array
ERR
must be at least
${\mathbf{M}}$ if
${\mathbf{MODE}}=2$, and at least
$1$ otherwise.
On exit: when
${\mathbf{MODE}}=2$,
ERR contains measures of correctness of the respective gradients. If there is no loss of significance (see
Section 8), then if
${\mathbf{ERR}}\left(i\right)$ is
$1.0$ the
$i$th user-supplied gradient is correct, whilst if
${\mathbf{ERR}}\left(i\right)$ is
$0.0$ the
$i$th gradient is incorrect. For values of
${\mathbf{ERR}}\left(i\right)$ between
$0.0$ and
$1.0$ the categorisation is less certain. In general, a value of
${\mathbf{ERR}}\left(i\right)>0.5$ indicates that the
$i$th gradient is probably correct.