Examples: Optimizing a Simple MIP Problem

ILOG CPLEX 11.0 User's Manual > Discrete Optimization > Solving Mixed Integer Programming Problems (MIP) > Examples: Optimizing a Simple MIP Problem

Examples: Optimizing a Simple MIP Problem

INDEX

These examples illustrate how to optimize a MIP with the ILOG CPLEX Component Libraries.

ilomipex1.cpp

The example derives from ilolpex8.cpp. Here are the differences between that linear program and this mixed integer program:

The problem to solve is slightly different. It appears in Stating a MIP Problem.
The routine populatebyrow added the variables, objective, and constraints to the model created by the method IloModel model(env).

MIPex1.java

Also available among the examples distributed with the product is a Java implementation of the same MIP.

MIPex1.cs and MIPex1.vb

Also available among the examples distributed with the product are a C#.NET and a Visual Basic.NET implementation of the same MIP.

mipex1.c

The example derives from lpex8.c. Here are the differences between that linear program and this mixed integer program:

The problem to solve is slightly different. It appears in Stating a MIP Problem.
The routine setproblemdata has a parameter, ctype, to set the types of the variables to indicate which ones must assume integer values. The routine CPXcopyctype associates this data with the problem that CPXcreateprob creates.
The example calls CPXmipopt to optimize the problem, rather than CPXlpopt.
The example calls the routines CPXgetstat, CPXgetobjval, CPXgetx, and CPXgetslack (instead of CPXsolution) to get a solution.

You do not get dual variables this way. If you want dual variables, you must do the following:

Use CPXchgprobtype to change the problem type to CPXPROB_FIXEDMILP.
Then call CPXprimopt to optimize that problem.
Then use CPXsolution to get a solution to the fixed problem.

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