Overview

Introduces the C++ class IloCplex.

CPLEX generally does not need to be involved while you create your model. However, after the model is set up, it is time to create your cplex object, that is, an instance of the class IloCplex, to be used to solve the model. IloCplex is a class derived from IloAlgorithm. There are other Concert Technology algorithm classes, also derived from IloAlgorithm, as documented in the CPLEX C++ API Reference Manual. Some models might also be solved by using other algorithms, such as the class IloCP for constraint programming, or by using a hybrid algorithm consisting of both CP Optimizer and CPLEX. Some models, on the other hand, cannot be solved with CPLEX.

The makeup of the model determines whether or not CPLEX can solve it. More precisely, in order to be handled by IloCplex objects, a model may only consist of modeling objects of the classes listed in Table 1.

Instances of IloConstraint extracted by CPLEX can be created in a variety of ways. Most often, they can be generated by means of overloaded C++ operators, such as == , <= , or >= , in the form expression1 operator expression2 . Instances of both IloConstraint and IloRange generated in that way may be built from either linear or quadratic expressions. Constraints and ranges may also include piecewise linear terms. (Other sections of this manual, not specific to C++, show you how to use quadratic expressions: Solving problems with a quadratic objective (QP) and Solving problems with quadratic constraints (QCP). Likewise, Using piecewise linear functions in optimization: a transport example shows you how to apply piecewise linear terms in a C++ application.)

For more detail about solving problems with IloCplex , see the following sections of this manual.

Table 1. Concert Technology modeling objects in C++
To model: Use:
numeric variables objects of the class IloNumVar , as long as they are not constructed with a list of feasible values
semi-continuous variables objects of the class IloSemiContVar
linear objective function an object of the class IloObjective with linear or piecewise linear expressions
quadratic objective function an object of the class IloObjective with quadratic expressions
linear constraints

objects of the class IloRange

(lower bound <= expression <= upper bound)

or

objects of the class IloConstraint

(expr1 relation expr2)

involving strictly linear or piecewise linear expressions
quadratic constraints objects of the class IloConstraint that contain quadratic expressions as well as linear expressions or piecewise linear expressions
logical constraints objects of the class IloConstraint or generated ranges with linear or piecewise linear expressions
variable type-conversions objects of the class IloConversion
special ordered sets of type 1 objects of the class IloSOS1
special ordered sets of type 2 objects of class IloSOS2

For an explanation of quadratic constraints, see Solving problems with quadratic constraints (QCP).

For more information about quadratic objective functions, see Solving problems with a quadratic objective (QP).

For examples of piecewise linear constraints, see Using piecewise linear functions in optimization: a transport example.

For more about logical constraints, see Logical constraints in optimization. This topic introduces the idea of generated ranges.

For a description of special ordered sets, see Using special ordered sets (SOS).