In this example, the entire problem definition is first described in the following PROC OPTMODEL step and is then saved as a QPS data set in the subsequent PROC OPTLSO step. In this case, no FCMP function needs to be defined.
proc optmodel;
var x{1..13} >= 0 <= 1;
for {i in 10..12} x[i].ub = 100;
min z = 5*sum{i in 1..4} x[i]
- 5*sum{i in 1..4} x[i]**2 - sum{i in 5..13} x[i];
con a1: 2*x[1] + 2*x[2] + x[10] + x[11] <= 10;
con a2: 2*x[1] + 2*x[3] + x[10] + x[12] <= 10;
con a3: 2*x[1] + 2*x[3] + x[11] + x[12] <= 10;
con a4: -8*x[1] + x[10] <= 0;
con a5: -8*x[2] + x[11] <= 0;
con a6: -8*x[3] + x[12] <= 0;
con a7: -2*x[4] - x[5] + x[10] <= 0;
con a8: -2*x[6] - x[7] + x[11] <= 0;
con a9: -2*x[8] - x[9] + x[12] <= 0;
save qps qpdata;
quit;
proc optlso
qpsdata = qpdata;
performance nthreads=2;
run;
Note that in this case the objective definition is taken directly from the QPS data set qpdata. Output 3.3.1 shows the output from running these steps.
Output 3.3.1: Using QPS Format
| Performance Information | |
|---|---|
| Execution Mode | Single-Machine |
| Number of Threads | 2 |
| Parallel Mode | Deterministic |
| Problem Summary | |
|---|---|
| Problem Type | QP |
| QPS Data Set | QPDATA |
| Number of Variables | 13 |
| Integer Variables | 0 |
| Continuous Variables | 13 |
| Number of Constraints | 9 |
| Linear Constraints | 9 |
| Nonlinear Constraints | 0 |
| Objective Definition Source | QPDATA |
| Objective Sense | Minimize |
| Solution Summary | |
|---|---|
| Solution Status | Function convergence |
| Objective | -15.0009821 |
| Infeasibility | 0.0009821034 |
| Iterations | 25 |
| Evaluations | 2620 |
| Cached Evaluations | 2757 |
| Global Searches | 1 |
| Population Size | 160 |
| Seed | 1 |