Algorithm used to solve continuous models
 Type: int
 Default value: -1
 Minimum value: -1
 Maximum value: 5

Algorithm used to solve continuous models or the initial root relaxation of a MIP model. Options are:

  • -1=automatic,
  • 0=primal simplex,
  • 1=dual simplex,
  • 2=barrier,
  • 3=concurrent,
  • 4=deterministic concurrent, and
  • 5=deterministic concurrent simplex.

Available settings and default behaviour depend on the model type or the type of the initial root relaxation. In the current release, the default Automatic (-1) setting will typically choose non-deterministic concurrent (Method=3) for an LP, barrier (Method=2) for a QP or QCP, and dual (Method=1) for the MIP root relaxation. If the size of the MIP root relaxation is large, then it will often select deterministic concurrent (Method=4) or deterministic concurrent simplex (Method=5).

Concurrent methods aren't available for QP and QCP. Only the simplex and barrier algorithms are available for continuous QP models. If you select barrier (Method=2) to solve the root of an MIQP model, then you need to also select barrier for the node relaxations (i.e. set NodeMethod=2). Only barrier is available for continuous QCP models. However if you choose LP relaxations for solving MIQCP, you can also select the simplex algorithms (Method=0 or 1).

Concurrent optimizers run multiple solvers on multiple threads simultaneously, and choose the one that finishes first. Method=3 and Method=4 will run dual simplex, barrier, and sometimes primal simplex (depending on the number of available threads). Method=5 will run both primal and dual simplex. The deterministic options (Method=4 and Method=5) give the exact same result each time, while Method=3 is often faster but can produce different optimal bases when run multiple times.

The default setting is rarely significantly slower than the best possible setting, so you generally won't see a big gain from changing this parameter. There are classes of models where one particular algorithm is consistently fastest, though, so you may want to experiment with different options when confronted with a particularly difficult model.

Note that if memory is tight on an LP model, you should consider using the dual simplex method (Method=1). The concurrent optimizer, which is typically chosen when using the default setting, consumes a lot more memory than dual simplex alone.

In multiobjective LP optimization:

  • The first objective is solved using LP defaults. It can be set by the user using the Method parameter.
  • Subsequent objectives are solved by default using primal simplex to allow for warm starting. The method used here can be controlled using MultiObjMethod.

For examples of how to query or modify parameter values from our different APIs, refer to our Parameter Examples.