ILOG CPLEX 11.0 User's Manual > Discrete Optimization

Discrete Optimization

This part focuses on algorithmic considerations about the ILOG CPLEX optimizers that solve problems formulated in terms of discrete variables, such as integer, Boolean, piecewise-linear, or semi-continuous variables. While default settings of ILOG CPLEX enable you to solve many problems without changing parameters, this part also documents features that enable you to tune performance. This part contains:

Solving Mixed Integer Programming Problems (MIP)
Solution Pool: Generating and Keeping Multiple Solutions
Using Special Ordered Sets (SOS)
Using Semi-Continuous Variables: a Rates Example
Using Piecewise Linear Functions in Optimization: a Transport Example
Logical Constraints in Optimization
Using Indicator Constraints
Using Logical Constraints: Food Manufacture 2
Early Tardy Scheduling
Using Column Generation: a Cutting Stock Example

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