The static objective (2) convergence criterion does not measure convergence of individual
variables. Instead, it measures
the significance of the changes in the objective function over recent SLP iterations.
It is applied when all the variables interacting with active constraints (those that have a
marginal value of at least
XSLP_MVTOL) have converged. The rationale is
that if the remaining unconverged variables are not involved in active constraints and if
the objective function is not changing significantly between iterations, then the solution
is more-or-less practical.
The variation in the objective function is defined as
Obj = MAXIter(Obj) - MINIter(Obj)
where
Iter is the
XSLP_OCOUNT most recent SLP iterations
and
Obj is the corresponding objective function value.
If
ABS(
Obj)
XSLP_OTOL_A
then the problem has converged on the absolute static objective (2) convergence criterion.
The static objective function (2) test is applied only if
XSLP_OCOUNT
is at least 2.