The OPTLP Procedure

Example 10.2 Using the Interior Point Solver

You can also solve the oil refinery problem described in Example 10.1 by using the interior point solver. You can create the input data set from an external MPS-format flat file by using the SAS macro %MPS2SASD or SAS DATA step code, both of which are described in Getting Started: OPTLP Procedure. You can use the following SAS code to solve the problem:

proc optlp data=ex1 
  objsense  = max 
  solver    = ii 
  primalout = ex1ipout 
  dualout   = ex1idout
  printfreq = 1;
run;

The optimal solution is displayed in Output 10.2.1.

Output 10.2.1 Interior Point Solver: Primal Solution Output

The OPTLP Procedure

Primal Solution

Obs	Objective Function ID	Variable Name	Variable Type	Objective Coefficient	Upper Bound	Variable Value	Reduced Cost
1	profit	a_l	D	-175	110	110.000	.
2	profit	a_h	D	-165	165	0.000	.
3	profit	br	D	-205	80	80.000	.
4	profit	na_l	N	0	1.7977E308	7.450	.
5	profit	na_i	N	0	1.7977E308	21.800	.
6	profit	h_o	N	0	1.7977E308	77.300	.
7	profit	j_1	N	350	1.7977E308	72.667	.
8	profit	j_2	N	350	1.7977E308	33.042	.

The iteration log is displayed in Output 10.2.2.

Output 10.2.2 Log: Solution Progress

The OPTLP Procedure

NOTE: The problem EX1 has 8 variables (0 free, 0 fixed).

NOTE: The problem has 6 constraints (3 LE, 3 EQ, 0 GE, 0 range).

NOTE: The problem has 19 constraint coefficients.

WARNING: The objective sense has been changed to maximization.

NOTE: The OPTLP presolver value AUTOMATIC is applied.

NOTE: The OPTLP presolver removed 3 variables and 3 constraints.

NOTE: The OPTLP presolver removed 6 constraint coefficients.

NOTE: The presolved problem has 5 variables, 3 constraints, and 13 constraint

coefficients.

NOTE: The ITERATIVE INTERIOR solver is called.

Primal Bound Dual

Iter Complement Duality Gap Infeas Infeas Infeas

0 6202.827182 578.875897 6.153481E-15 0 31.724076

1 2098.979622 131.203992 1.464821E-14 2.874219E-17 10.084119

2 56.777181 37.508992 1.794241E-14 6.400722E-17 0.301831

3 1.655583 0.291985 2.628969E-14 2.994678E-17 0.045333

4 0.590964 0.108785 1.561351E-14 2.024086E-17 0.000453

5 0.006051 0.001012 1.878018E-14 2.504275E-17 0.000004576

6 0.000060511 0.000010109 1.686314E-14 1.841004E-17 4.5764285E-8

7 0.000000605 0.000000101 1.746385E-14 4.482743E-17 4.576428E-10

NOTE: Optimal.

NOTE: Objective = 1347.91653.

NOTE: The data set WORK.EX1IPOUT has 8 observations and 10 variables.

NOTE: The data set WORK.EX1IDOUT has 6 observations and 10 variables.