XSLP_MTOL_A

Absolute effective matrix element convergence tolerance

Double

-1.0

The absolute effective matrix element convergence criterion assesses the change in the effect of a coefficient in a constraint. The effect of a coefficient is its value multiplied by the activity of the column in which it appears.

E = X * C

where X is the activity of the matrix column in which the coefficient appears, and C is the value of the coefficient. The linearization approximates the effect of the coefficient as

E = X * C₀ + δX * C'₀

where V is as before, C₀ is the value of the coefficient C calculated using the assumed values for the variables and C'₀ is the value of

∂C

∂X

calculated using the assumed values for the variables.
If C₁ is the value of the coefficient C calculated using the actual values for the variables, then the error in the effect of the coefficient is given by

δE = X * C₁ - (X * C₀ + δX * C'₀)

If δE < X * XSLP_MTOL_A
then the variable has passed the absolute effective matrix element convergence criterion for this coefficient.
If a variable which has not converged on strict (closure or delta) criteria passes the (relative or absolute) impact or matrix criteria for all the coefficients in which it appears, then it is deemed to have converged.

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

XSLPmaxim, XSLPminim

XSLP_ITOL_A, XSLP_ITOL_R, XSLP_MTOL_R, XSLP_STOL_A, XSLP_STOL_R