Choosing an Optimizer

Choosing an Optimizer

The solution algorithm used in the solve() methods can be controlled and if necessary tailored to the particular needs of the model. The most important control is that of selecting the optimizer. For solving the active model, ILOG CPLEX solves one LP, one QP or a series of LPs or QPs:

A single LP is solved if IloCplex.isMIP() and IloCplex.isQuadratic() return false. This is the case if the active model does not include:

IloCplex provides several optimizing algorithms to solve LPs.

A single QP is solved if IloCplex.isMIP() returns false and IloCplex.isQuadratic() returns true. This is the case if the active model contains a quadratic (and positive semi-definite) objective but does not contain:

As in the case of LPs, IloCplex provides several optimizing algorithms to solve QPs.

A series of LPs or QPs is solved if the active model is a MIP, which can be recognized by IloCplex.isMIP() returning true. This is the case if the model contains any of the objects excluded for single QPs above. If a MIP contains a purely linear objective function, that is IloCplex.isQuadratic() returns false, the problem is more precisely called an MILP. Otherwise it is called an MIQP. MIPs are solved using branch & cut search, which is described in more detail in Chapter 8, Solving Mixed Integer Programming Problems.

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