XSLP_OTOL_R

Relative static objective (2) convergence tolerance

Double

-1.0

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

δObj = MAX_Iter(Obj) - MIN_Iter(Obj)

where Iter is the XSLP_OCOUNT most recent SLP iterations and Obj is the corresponding objective function value.
If ABS(δObj) ≤ AVG_Iter(Obj)*XSLP_OTOL_R
then the problem has converged on the relative static objective (2) convergence criterion.
The static objective function (2) test is applied only if XSLP_OCOUNT is at least 2.

When the value is set to be negative, the value is adjusted automatically by SLP, based on the optimality target XSLP_VALIDATIONTARGET_K. Good values for the control are usually fall between 1e-3 and 1e-6.

XSLPmaxim, XSLPminim

XSLP_OCOUNT, XSLP_OTOL_A