The static objective (3) convergence criterion does not measure convergence of
individual variables, and in fact does not in any way imply that the solution has converged.
However, it is sometimes useful to be able to terminate an optimization once the objective
function appears to have stabilized. One example is where a set of possible schedules are
being evaluated and initially only a good estimate of the likely objective function value is
required, to eliminate the worst candidates.
The variation in the objective function is defined as
Obj = MAXIter(Obj) - MINIter(Obj)
where
Iter is the
XSLP_VCOUNT most recent SLP iterations
and
Obj is the corresponding objective function value.
If
ABS(
Obj)
AVGIter(Obj) * XSLP_VTOL_R
then the problem has converged on the absolute static objective function (3) criterion.
The static objective function (3) test is applied only if after at least
XSLP_VLIMIT
+
XSLP_SBSTART SLP iterations have taken place and only if
XSLP_VCOUNT is at least 2. Where step bounding is
being used, this ensures that the test is not applied until after step bounding has been introduced.