Choose an Optimizer

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Choose an Optimizer

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After you have instantiated and populated a problem object, you solve it by calling one of the optimizers available in the ILOG CPLEX Component Libraries. Your choice of optimizer depends on the type of problem:

The primal simplex, dual simplex, and primal-dual barrier methods can be used to solve linear and quadratic programs.
The barrier optimizer can also be used to solve quadratically constrained programming problems.
The network optimizer is appropriate for solving linear and quadratic programs with large embedded networks.
The branch & cut algorithm should be used if the problem contains discrete components (binary, integer, or semi-continuous variables, piecewise linear objective, or SOS sets).

In ILOG CPLEX, there are many possible parameter settings for each optimizer. Generally, the default parameter settings are best for linear programming and quadratic programming problems, but Chapter 8, Solving LPs: Simplex Optimizers and Chapter 11, Solving Problems with a Quadratic Objective (QP) offer more detail about improving performance with respect to these problems. Integer programming problems are more sensitive to specific parameter settings, so you may need to experiment with them, as suggested in Chapter 13, Solving Mixed Integer Programming Problems (MIP).

In either case, the Interactive Optimizer in ILOG CPLEX lets you try different parameter settings and different optimizers to determine the best optimization procedure for your particular application. From what you learn by experimenting with commands in the Interactive Optimizer, you can more readily choose which method or routine from the Component Libraries to call in your application.

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