Output Data Sets
 
 This subsection describes the PRIMALOUT= and  DUALOUT= output data sets. If the 
 SAVE_ONLY_IF_OPTIMAL option is not specified, the output data sets will not contain an intermediate solution.
 
Definitions of Variables in the  PRIMALOUT= Data Set
 The PRIMALOUT= data set contains the primal solution to the QP model. The variables in the data set have the following names and meanings.
 
- _OBJ_ID_
- 
 
 specifies the name of the objective function. This is particularly useful when there are multiple objective functions, in which case each objective function has a unique name. 
 See "ROWS Section" for details.
 
Note: PROC OPTQP does not support simultaneous optimization of multiple objective functions in this release.
 
- _RHS_ID_
- 
 
 specifies the  name of the variable containing the right-hand-side value of each constraint. 
 See "ROWS Section" for details.
 
- _VAR_
- 
 
 specifies the name of the decision variable.
 
- _TYPE_
- 
 
 specifies the type of the decision variable. _TYPE_ can take one of the following values: 
 
  
- N
 
- nonnegative variable 
 		
- D
 
- bounded variable with either lower or upper bound 
 		
- F
 
- free variable 
 		
- X
 
- fixed variable 
 		
- O
 
- other 
 
 
- _OBJCOEF_
- 
 
 specifies the coefficient of the decision variable in the linear component of the objective function.
 
- _LBOUND_
- 
 
 specifies the  lower bound on the decision variable.
 
- _UBOUND_
- 
 
 specifies the  upper bound on the decision variable.
 
- _VALUE_
- 
 
 specifies the value of the decision variable.
 
- _STATUS_
- 
 
 specifies the status of the decision variable. _STATUS_ can indicate one of the following two cases:
 
   
- O
 
- QP problem is optimal. 
 		
- I
 
- QP problem could be infeasible or unbounded,  or PROC OPTQP was not able to solve the problem. 
 
Definitions of Variables in the  DUALOUT= Data Set
 The DUALOUT= data set contains the dual solution to the QP model. Information about the objective rows of the QP problems is not included. The variables in the data set have the following names and meanings.
 
- _OBJ_ID_
- 
 
 specifies the  name of the objective function. This is particularly useful when there are multiple objective functions, in which case each objective function has a unique name. 
 See "ROWS Section" for details.
 
Note: PROC OPTQP does not support simultaneous optimization of multiple objective functions in this release.
 
- _RHS_ID_
- 
 
 specifies the  name of the variable containing the right-hand-side value of each constraint. 
 See "ROWS Section" for details.
 
- _ROW_
- 
 
 specifies the name of the constraint. See "ROWS Section" for details.
 
- _TYPE_
- 
 
 specifies the  type of the constraint. _TYPE_ can take one of the following values: 
 	
  
- L
 
- "less than or equals" constraint 
 		
- E
 
- equality constraint 
 		
- G
 
- "greater than or equals" constraint 
 		
- R
 
- ranged constraint (both "less than or equals" and "greater than or equals") 
 	
 See "ROWS Section" and "RANGES Section (Optional)" for details.
  
- _RHS_
- 
 
 specifies the  value of the right-hand side of the constraints. It takes a missing value for a ranged constraint.
 
- _L_RHS_
- 
 
 specifies the lower bound of a ranged constraint. It takes a missing value for a non-ranged constraint.
 
- _U_RHS_
- 
 
 specifies the upper bound of a ranged constraint. It takes a missing value for a non-ranged constraint.
 
- _VALUE_
- 
 
 specifies the value of the dual variable associated with the constraint.
 
- _STATUS_
- 
 
 specifies the status of the constraint. 
 _STATUS_ can indicate one of the following two cases:
 
   
- O
 
- QP problem is optimal. 
 		
- I
 
- QP problem could be infeasible or unbounded,  or PROC OPTQP was not able to solve the problem. 
 
- _ACTIVITY_
- 
 
 specifies the value of a constraint. In other words, the value of _ACTIVITY_ 
 for the  th constraint would be equal to th constraint would be equal to , where , where refers to the refers to the th row of the constraints matrix and th row of the constraints matrix and denotes the vector of current decision variable values. denotes the vector of current decision variable values.
 
Copyright © 2008 by SAS Institute Inc., Cary, NC, USA. All rights reserved.