> Languages and APIs > ILOG Concert Technology for C++ Users > Solving Concert Technology Models with IloCplex > Controlling the Optimizers |
Controlling the Optimizers |
INDEX PREVIOUS NEXT |
Though ILOG CPLEX defaults will prove sufficient to solve most problems, ILOG CPLEX offers a variety of parameters to control various algorithmic choices. ILOG CPLEX parameters can assume values of type bool
, num
, int
, and string
. IloCplex
provides four categories of parameters that are listed in the nested enumeration types IloCplex::BoolParam
, IloCplex::IntParam, IloCplex::NumParam, IloCplex::StringParam
.
To access the current value of a parameter that interests you from Concert Technology, use the method getParam
. To access the default value of a parameter, use the method getDefault
. Use the methods getMin
and g
etMax
to access the minimum and maximum values of num
and int
type parameters.
Some integer parameters are tied to nested enumerations that define symbolic constants for the values the parameter may assume. Table 1.3 summarizes those parameters and their enumeration types.
Only the parameter IloCplex::MIPEmphasis
may be of importance for general use.
There are, of course, routines in Concert Technology to set these parameters. Use the following methods to set the values of ILOG CPLEX parameters:
For example, the numerical parameter IloCplex::EpOpt
controlling the optimality tolerance for the simplex algorithms can be set to 0.0001 by calling
The reference manual ILOG CPLEX Parameters documents the type of each parameter (bool
, int
, num
, string
) along with the Concert Technology enumeration value, symbolic constant, and reference number representing the parameter.
The method setDefaults
resets all parameters (except the log file) to their default values, including the ILOG CPLEX callback functions. This routine resets the callback functions to NULL
.
When solving MIPs, additional controls of the solution process are provided. Priority orders and branching directions can be used to control the branching in a static way. These are discussed in Heuristics. These controls are static in the sense that they allow you to control the solution process based on data that does not change during the solution and can thus be set up before solving the model.
Dynamic control of the solution process of MIPs is provided through goals or control callbacks. They are discussed in Using Goals, and in Using Callbacks. Goals and callbacks allow you to control the solution process based on information that is generated during the solution process. Goals and Callbacks: a Comparison contrasts the advantages of each approach.
Copyright © 1987-2003 ILOG, S.A. All rights reserved. Legal terms. | PREVIOUS NEXT |