limit on superbasics.

CONOPT uses a reduced gradient algorithm and performs its optimization in a space of “Superbasic” variables. It needs a square matrix of size the number of superbasic variables for estimated second order information. Since this matrix can be fairly large, CONOPT places a limit that by default is at least 500 rows and columns. For larger models CONOPT reserves around 25% of the memory needed for other purposes to this matrix. If you have a model with many superbasic variables it may be advantageous to increase this limit.

Note

If you provide second order information, through one of the 2DLagr, 2DDir, or 2DDirLag routines, then it is usually not worth while to increase MaxSup.

The limit can also be defined in an options file or options routine by setting LFNSUP.

Parameters

maxsup

the limit on superbasics