The Mixed Integer Linear Programming Solver
| Problem Statistics |
Optimizers can encounter difficulty when solving poorly formulated models. Information about data magnitude provides a simple gauge to determine how well a model is formulated. For example, a model whose constraint matrix contains one very large entry (on the order of 
) can cause difficulty when the remaining entries are single-digit numbers. The PRINTLEVEL=2 option in the OPTMODEL procedure causes the ODS table "ProblemStatistics" to be generated when the MILP solver is called. This table provides basic data magnitude information that enables you to improve the formulation of your models.
The example output in Figure 6.4 demonstrates the contents of the ODS table "ProblemStatistics."
| ProblemStatistics |
| Obs | Label1 | cValue1 | nValue1 |
|---|---|---|---|
| 1 | Number of Constraint Matrix Nonzeros | 8 | 8.000000 |
| 2 | Maximum Constraint Matrix Coefficient | 3 | 3.000000 |
| 3 | Minimum Constraint Matrix Coefficient | 1 | 1.000000 |
| 4 | Average Constraint Matrix Coefficient | 1.875 | 1.875000 |
| 5 | . | ||
| 6 | Number of Objective Nonzeros | 3 | 3.000000 |
| 7 | Maximum Objective Coefficient | 4 | 4.000000 |
| 8 | Minimum Objective Coefficient | 2 | 2.000000 |
| 9 | Average Objective Coefficient | 3 | 3.000000 |
| 10 | . | ||
| 11 | Number of RHS Nonzeros | 3 | 3.000000 |
| 12 | Maximum RHS | 7 | 7.000000 |
| 13 | Minimum RHS | 4 | 4.000000 |
| 14 | Average RHS | 5.3333333333 | 5.333333 |
| 15 | . | ||
| 16 | Maximum Number of Nonzeros per Column | 3 | 3.000000 |
| 17 | Minimum Number of Nonzeros per Column | 2 | 2.000000 |
| 18 | Average Number of Nonzeros per Column | 2 | 2.000000 |
| 19 | . | ||
| 20 | Maximum Number of Nonzeros per Row | 3 | 3.000000 |
| 21 | Minimum Number of Nonzeros per Row | 2 | 2.000000 |
| 22 | Average Number of Nonzeros per Row | 2 | 2.000000 |
The variable names in the ODS table "ProblemStatistics" are Label
, cValue, and nValue.