Linear ProgrammingLast updated: 01/01/2023
- Problem: name of the benchmark problem.
- f(x): objective function value at solution.
- eps(C): convergency tolerance.
- eps(F): feasibility tolerance.
- # Iter.: number of iterations used by the solver.
- # x: number of variables in the benchmark problem.
- # b: number of simple bound constraints in the benchmark problem.
- # LC: number of linear constraints in the benchmark problem.
- # NLC: number of non-linear constraints in the benchmark problem.
- # f(x)-O: number of function calls used by the solver to evaluate objective function.
- # g(x)-O: number of function calls used by the solver to evaluate the gradient of the objective function.
- # H(x)-O: number of function calls used by the solver to evaluate the Hessian of the objective function.
- # f(x)-C: number of function calls used by the solver to evaluate the nonlinear constraint function.
- # g(x)-C: number of function calls used by the solver to evaluate the gradient of the nonlinear constraint function.
- Time(ms.): time in milliseconds used by the solver to find the solution.
CUTE linear programming benchmark problems.
- Method choice:
Simplex
- Max number of iterations:
2000
| Problem |
f(x) |
eps(C) |
eps(F) |
# Iter. |
# x |
# b |
# LC |
# NLC |
# f(x)-O |
# g(x)-O |
# H(x)-O |
# f(x)-C |
# g(x)-C |
Time(ms.) |
| AGG |
-3.599e+007 |
1.0e-008 |
1.0e-011 |
109 |
163 |
163 |
488 |
0 |
1 |
1 |
0 |
1 |
1 |
1700.0 |
| DEGENLPA |
3.060e+000 |
1.0e-008 |
1.0e-011 |
23 |
20 |
20 |
15 |
0 |
1 |
1 |
0 |
1 |
1 |
85.2 |
| DEGENLPB |
-3.073e+001 |
1.0e-008 |
1.0e-011 |
24 |
20 |
20 |
15 |
0 |
1 |
1 |
0 |
1 |
1 |
82.9 |
| EXTRASIM |
0.000e+000 |
1.0e-008 |
1.0e-011 |
2 |
2 |
1 |
1 |
0 |
1 |
1 |
0 |
1 |
1 |
81.3 |
| GOFFIN |
0.000e+000 |
1.0e-008 |
1.0e-011 |
51 |
51 |
0 |
50 |
0 |
1 |
1 |
0 |
1 |
1 |
220.1 |
| MAKELA4 |
0.000e+000 |
1.0e-008 |
1.0e-011 |
38 |
21 |
0 |
40 |
0 |
1 |
1 |
0 |
1 |
1 |
163.7 |
| OET1 |
5.382e-001 |
1.0e-008 |
1.0e-011 |
523 |
3 |
0 |
1002 |
0 |
1 |
1 |
0 |
1 |
1 |
80880.0 |
| OET3 |
4.505e-003 |
1.0e-008 |
1.0e-011 |
676 |
4 |
0 |
1002 |
0 |
1 |
1 |
0 |
1 |
1 |
86890.0 |
| PT |
1.905e-001 |
1.0e-008 |
1.0e-011 |
571 |
2 |
0 |
501 |
0 |
1 |
1 |
0 |
1 |
1 |
5131.0 |
| SIMPLLPA |
1.000e+000 |
1.0e-008 |
1.0e-011 |
4 |
2 |
2 |
2 |
0 |
1 |
1 |
0 |
1 |
1 |
80.9 |
| SIMPLLPB |
1.100e+000 |
1.0e-008 |
1.0e-011 |
5 |
2 |
2 |
3 |
0 |
1 |
1 |
0 |
1 |
1 |
80.6 |
| SIPOW1 |
-7.104e-001 |
1.0e-008 |
1.0e-011 |
49 |
2 |
0 |
2000 |
0 |
1 |
1 |
0 |
1 |
1 |
4764.0 |
| SIPOW1M |
-7.115e-001 |
1.0e-008 |
1.0e-011 |
50 |
2 |
0 |
2000 |
0 |
1 |
1 |
0 |
1 |
1 |
4792.0 |
| SIPOW2 |
-7.997e-001 |
1.0e-008 |
1.0e-011 |
48 |
2 |
0 |
2000 |
0 |
1 |
1 |
0 |
1 |
1 |
5399.0 |
| SIPOW2M |
-8.733e-001 |
1.0e-008 |
1.0e-011 |
71 |
2 |
0 |
2000 |
0 |
1 |
1 |
0 |
1 |
1 |
8429.0 |
| SIPOW3 |
5.905e-001 |
1.0e-008 |
1.0e-011 |
50 |
4 |
0 |
2000 |
0 |
1 |
1 |
0 |
1 |
1 |
4301.0 |
| SIPOW4 |
2.724e-001 |
1.0e-008 |
1.0e-011 |
167 |
4 |
0 |
2000 |
0 |
1 |
1 |
0 |
1 |
1 |
17180.0 |
| SSEBLIN |
1.617e+007 |
1.0e-008 |
1.0e-011 |
178 |
194 |
194 |
72 |
0 |
1 |
1 |
0 |
1 |
1 |
701.5 |
| SUPERSIM |
6.667e-001 |
1.0e-008 |
1.0e-011 |
3 |
2 |
1 |
2 |
0 |
1 |
1 |
0 |
1 |
1 |
76.3 |
| TFI2 |
6.490e-001 |
1.0e-008 |
1.0e-011 |
115 |
3 |
0 |
101 |
0 |
1 |
1 |
0 |
1 |
1 |
395.5 |
CUTE linear programming benchmark problems.
- Method choice:
Active-Set
- Max number of iterations:
2000
| Problem |
f(x) |
eps(C) |
eps(F) |
# Iter. |
# x |
# b |
# LC |
# NLC |
# f(x)-O |
# g(x)-O |
# H(x)-O |
# f(x)-C |
# g(x)-C |
Time(ms.) |
| AGG |
-3.599e+007 |
1.0e-008 |
1.0e-009 |
574 |
163 |
163 |
488 |
0 |
1 |
1 |
0 |
1 |
1 |
693.4 |
| DEGENLPA |
3.060e+000 |
1.0e-008 |
1.0e-011 |
64 |
20 |
20 |
15 |
0 |
1 |
1 |
0 |
1 |
1 |
87.0 |
| DEGENLPB |
-3.073e+001 |
1.0e-008 |
1.0e-011 |
66 |
20 |
20 |
15 |
0 |
1 |
1 |
0 |
1 |
1 |
86.1 |
| EXTRASIM |
0.000e+000 |
1.0e-008 |
1.0e-011 |
0 |
2 |
1 |
1 |
0 |
1 |
1 |
0 |
1 |
1 |
77.3 |
| GOFFIN |
7.550e-015 |
1.0e-008 |
1.0e-011 |
50 |
51 |
0 |
50 |
0 |
1 |
1 |
0 |
1 |
1 |
102.1 |
| MAKELA4 |
1.776e-015 |
1.0e-008 |
1.0e-011 |
21 |
21 |
0 |
40 |
0 |
1 |
1 |
0 |
1 |
1 |
93.7 |
| OET1 |
5.382e-001 |
1.0e-008 |
1.0e-011 |
1917 |
3 |
0 |
1002 |
0 |
1 |
1 |
0 |
1 |
1 |
292.5 |
| OET3 |
4.505e-003 |
1.0e-008 |
1.0e-011 |
1238 |
4 |
0 |
1002 |
0 |
1 |
1 |
0 |
1 |
1 |
273.2 |
| PT |
1.784e-001 |
1.0e-008 |
1.0e-011 |
1230 |
2 |
0 |
501 |
0 |
1 |
1 |
0 |
1 |
1 |
19.9 |
| SIMPLLPA |
1.000e+000 |
1.0e-008 |
1.0e-011 |
4 |
2 |
2 |
2 |
0 |
1 |
1 |
0 |
1 |
1 |
80.7 |
| SIMPLLPB |
1.100e+000 |
1.0e-008 |
1.0e-011 |
4 |
2 |
2 |
3 |
0 |
1 |
1 |
0 |
1 |
1 |
80.0 |
| SIPOW1 |
-1.000e+000 |
1.0e-008 |
1.0e-011 |
590 |
2 |
0 |
2000 |
0 |
1 |
1 |
0 |
1 |
1 |
220.1 |
| SIPOW1M |
-1.000e+000 |
1.0e-008 |
1.0e-011 |
592 |
2 |
0 |
2000 |
0 |
1 |
1 |
0 |
1 |
1 |
225.0 |
| SIPOW2 |
-1.000e+000 |
1.0e-008 |
1.0e-011 |
296 |
2 |
0 |
2000 |
0 |
1 |
1 |
0 |
1 |
1 |
149.0 |
| SIPOW2M |
-1.000e+000 |
1.0e-008 |
1.0e-011 |
296 |
2 |
0 |
2000 |
0 |
1 |
1 |
0 |
1 |
1 |
163.4 |
| SIPOW3 |
5.347e-001 |
1.0e-008 |
1.0e-011 |
126 |
4 |
0 |
2000 |
0 |
1 |
1 |
0 |
1 |
1 |
129.8 |
| SIPOW4 |
2.724e-001 |
1.0e-008 |
1.0e-011 |
304 |
4 |
0 |
2000 |
0 |
1 |
1 |
0 |
1 |
1 |
110.9 |
| SSEBLIN |
1.617e+007 |
1.0e-008 |
1.0e-011 |
232 |
194 |
194 |
72 |
0 |
1 |
1 |
0 |
1 |
1 |
414.9 |
| SUPERSIM |
6.667e-001 |
1.0e-008 |
1.0e-011 |
0 |
2 |
1 |
2 |
0 |
1 |
1 |
0 |
1 |
1 |
80.6 |
| TFI2 |
6.490e-001 |
1.0e-008 |
1.0e-011 |
206 |
3 |
0 |
101 |
0 |
1 |
1 |
0 |
1 |
1 |
90.0 |