XSLP_VTOL_A, SLPVTOL_A

Absolute static objective (3) convergence tolerance

Double

-1.0

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

δObj = MAX_Iter(Obj) - MIN_Iter(Obj)

where Iter is the XSLP_VCOUNT most recent SLP iterations and Obj is the corresponding objective function value.
If ABS(δObj) ≤ XSLP_VTOL_A
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.

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_SBSTART, XSLP_VCOUNT, XSLP_VLIMIT, XSLP_VTOL_R