XSLP_SCALE

When to re-scale the SLP problem

Integer

0	No re-scaling.
1	Re-scale every SLP iteration up to XSLP_SCALECOUNT iterations after the end of barrier optimization.
2	Re-scale every SLP iteration up to XSLP_SCALECOUNT iterations in total.
3	Re-scale every SLP iteration until primal simplex is automatically invoked.
4	Re-scale every SLP iteration.
5	Re-scale every XSLP_SCALECOUNT SLP iterations.
6	Re-scale every XSLP_SCALECOUNT SLP iterations after the end of barrier optimization.

1

During the SLP optimization, matrix entries can change considerably in magnitude, even when the formulae in the coefficients are not very nonlinear. Re-scaling of the matrix can reduce numerical errors, but may increase the time taken to achieve convergence.

XSLPmaxim, XSLPminim

XSLP_SCALECOUNT