Optimizer options
Introduces the options available in CPLEX.
This manual explains how to use the LP algorithms that are part of CPLEX. The QP, QCP, and MIP problem types are based on the LP concepts discussed here, and the extensions to build and solve such problems are explained in the CPLEX User’s Manual.
Default settings will result in a call to an optimizer
that is appropriate to the class of problem you are solving. However,
you may wish to choose a different optimizer for special purposes.
An LP or QP problem can be solved using any of the following CPLEX
optimizers: dual simplex, primal simplex, barrier, and perhaps also
the network optimizer (if the problem contains an extractable network
substructure). Pure network models are all solved by the network optimizer.
QCP models, including the special case of SOCP models, are all solved
by the barrier optimizer. MIP models are all solved by the mixed integer
optimizer, which in turn may invoke any of the LP or QP optimizers
in the course of its computation. The table titled Table 1 summarizes
these possible choices.
| LP | Network | QP | QCP | MIP | |
|---|---|---|---|---|---|
| Dual Optimizer | yes | yes | |||
| Primal Optimizer | yes | yes | |||
| Barrier Optimizer | yes | yes | yes | ||
| Mixed Integer Optimizer | yes | ||||
| Network Optimizer | Note 1 | yes | Note 1 | ||
| Note 1: The problem must contain an extractable network substructure. | |||||
The choice of optimizer or other parameter settings may have a very large effect on the solution speed of your particular class of problem. The CPLEX User's Manual describes the optimizers, provides suggestions for maximizing performance, and notes the features and algorithmic parameters unique to each optimizer.