The NLP Procedure |
This example illustrates the INQUAD= option for specifying a quadratic programming problem:
Suppose that , and , , and .
You specify the constant and the Hessian in the data set QUAD1. Notice that the _TYPE_ variable contains the keywords that identify how the procedure should interpret the observations.
data quad1; input _type_ $ _name_ $ x1 x2; datalines; const . -100 -100 quad x1 0.4 0 quad x2 0 4 ;
You specify the QUAD1 data set with the INQUAD= option. Notice that the names of the variables in the QUAD1 data set and the _NAME_ variable match the names of the parameters in the PARMS statement.
proc nlp inquad=quad1 all; min ; parms x1 x2 = -1; bounds 2 <= x1 <= 50, -50 <= x2 <= 50; lincon 10 <= 10 * x1 - x2; run;
Alternatively, you can use a sparse format for specifying the matrix, eliminating the zeros. You use the special variables _ROW_, _COL_, and _VALUE_ to give the nonzero row and column names and value.
data quad2; input _type_ $ _row_ $ _col_ $ _value_; datalines; const . . -100 quad x1 x1 0.4 quad x2 x2 4 ;
You can also include the constraints in the QUAD data set. Notice how the _TYPE_ variable contains keywords that identify how the procedure is to interpret the values in each observation.
data quad3; input _type_ $ _name_ $ x1 x2 _rhs_; datalines; const . -100 -100 . quad x1 0.02 0 . quad x2 0.00 2 . parms . -1 -1 . lowerbd . 2 -50 . upperbd . 50 50 . ge . 10 -1 10 ;
proc nlp inquad=quad3; min ; parms x1 x2; run;
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