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The OPTMILP Procedure
ODS Tables

PROC OPTMILP creates two Output Delivery System (ODS) tables by default unless you specify a value other than 1 for the PRINTLEVEL= option. The first table, "ProblemSummary," is a summary of the input MILP problem. The second table, "SolutionSummary," is a brief summary of the attempt to solve the problem. You can refer to these tables when using ODS. An example output of PROC OPTMILP is shown in Figure 18.5 (Problem Summary) and Figure 18.6 (Solution Summary). For more information about ODS, see SAS Output Delivery System: User’s Guide.

If you specify a value of 2 for the PRINTLEVEL= option, then a third table, "ProblemStatistics," is produced. This table contains information about the problem data. See the section Problem Statistics for more information.


Table 18.11 ODS Tables Produced by PROC OPTMILP

ODS Table Name

Description

PRINTLEVEL=

ProblemSummary

Summary of the input MILP problem

1 (default)

SolutionSummary

Summary of the solution status

1 (default)

ProblemStatistics

Description of input problem data

2

 




    Figure 18.5
     Example PROC OPTMILP Output: Problem Summary











  






The OPTMILP Procedure
















Problem Summary






Problem Name
EX_MIP




Objective Sense
Minimization




Objective Function
COST




RHS
RHS




 
 




Number of Variables
3




Bounded Above
0




Bounded Below
0




Bounded Above and Below
3




Free
0




Fixed
0




Binary
3




Integer
0




 
 




Number of Constraints
3




LE (<=)
2




EQ (=)
0




GE (>=)
1




Range
0




 
 




Constraint Coefficients
8

















 




    Figure 18.6
     Example PROC OPTMILP Output: Solution Summary











  






The OPTMILP Procedure
















Solution Summary






Objective Function
COST




Solution Status
Optimal




Objective Value
-7




 
 




Relative Gap
0




Absolute Gap
0




Primal Infeasibility
0




Bound Infeasibility
0




Integer Infeasibility
0




 
 




Best Bound
.




Nodes
0




Iterations
0




Presolve Time
0.00




Solution Time
0.00















 
You can create output data sets from these tables by using the ODS OUTPUT statement. The output data sets from the preceding example are displayed in Figure 18.7 and Figure 18.8, where you can also find variable names for the tables used in the ODS template of the OPTMILP procedure. 




    Figure 18.7
     ODS Output Data Set: Problem Summary











Problem Summary






















Obs
Label1
cValue1
nValue1






 1
Problem Name
EX_MIP
           .




 2
Objective Sense
Minimization
           .




 3
Objective Function
COST
           .




 4
RHS
RHS
           .




 5
 
 
           .




 6
Number of Variables
3
    3.000000




 7
Bounded Above
0
           0




 8
Bounded Below
0
           0




 9
Bounded Above and Below
3
    3.000000




10
Free
0
           0




11
Fixed
0
           0




12
Binary
3
    3.000000




13
Integer
0
           0




14
 
 
           .




15
Number of Constraints
3
    3.000000




16
LE (<=)
2
    2.000000




17
EQ (=)
0
           0




18
GE (>=)
1
    1.000000




19
Range
0
           0




20
 
 
           .




21
Constraint Coefficients
8
    8.000000


















 




    Figure 18.8
     ODS Output Data Set: Solution Summary











Solution Summary






















Obs
Label1
cValue1
nValue1






 1
Objective Function
COST
           .




 2
Solution Status
Optimal
           .




 3
Objective Value
-7
   -7.000000




 4
 
 
           .




 5
Relative Gap
0
           0




 6
Absolute Gap
0
           0




 7
Primal Infeasibility
0
           0




 8
Bound Infeasibility
0
           0




 9
Integer Infeasibility
0
           0




10
 
 
           .




11
Best Bound
.
           .




12
Nodes
0
           0




13
Iterations
0
           0




14
Presolve Time
        0.00
           0




15
Solution Time
        0.00
           0


















 


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 OPTMILP procedure causes the ODS table "ProblemStatistics" to be generated. This table provides basic data magnitude information that enables you to improve the formulation of your models. 
The example output in Figure 18.9 demonstrates the contents of the ODS table "ProblemStatistics." 




    Figure 18.9
     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. 






















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