API Examples¶
Suboptimal solutions¶
Given a solution, we can use the SolCheck.test_sol() function to test
it.
import gurobipy as gp
import gurobi_modelanalyzer as gma
m = gp.read("afiro.mps")
sol = {m.getVarByName("X01"): 78, m.getVarByName("X22"): 495}
sc = gma.SolCheck(m)
sc.test_sol(sol)
print(f"Solution Status: {sc.Status}")
Will print:
Read MPS format model from file afiro.mps
Reading time = 0.00 seconds
AFIRO: 27 rows, 32 columns, 83 nonzeros
Gurobi Optimizer version 11.0.3 build v11.0.3rc0
Thread count: 8 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 27 rows, 32 columns and 83 nonzeros
Model fingerprint: 0x540c3b7f
Coefficient statistics:
Matrix range [1e-01, 2e+00]
Objective range [3e-01, 1e+01]
Bounds range [8e+01, 5e+02]
RHS range [4e+01, 5e+02]
Presolve removed 19 rows and 22 columns
Presolve time: 0.03s
Presolved: 8 rows, 10 columns, 27 nonzeros
Iteration Objective Primal Inf. Dual Inf. Time
0 -4.5969189e+02 2.146875e+00 0.000000e+00 0s
3 -4.5969189e+02 0.000000e+00 0.000000e+00 0s
Solved in 3 iterations and 0.05 seconds (0.00 work units)
Optimal objective -4.596918857e+02
Solution is feasible for feasibility tolerance of 1e-06
Solution Status: 13
We can check this solution against the optimal one by calling
SolCheck.optimize().
sc.optimize()
for v in sol.keys():
print(f"{v.VarName}: Fixed value: {sol[v]}, Computed value: {v.X}")
Produces
Comparing quality with original solution
Gurobi Optimizer version 11.0.3 build v11.0.3rc0
Thread count: 8 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 27 rows, 32 columns and 83 nonzeros
Coefficient statistics:
Matrix range [1e-01, 2e+00]
Objective range [3e-01, 1e+01]
Bounds range [0e+00, 0e+00]
RHS range [4e+01, 5e+02]
Iteration Objective Primal Inf. Dual Inf. Time
0 -3.1277714e+30 1.240950e+31 3.127771e+00 0s
4 -4.6475314e+02 0.000000e+00 0.000000e+00 0s
Solved in 4 iterations and 0.00 seconds (0.00 work units)
Optimal objective -4.647531429e+02
Objectives:
Fixed: -459.6919
Optimal: -464.7531
Difference: -5.0613
X01: Fixed value: 78, Computed value: 80.0
X22: Fixed value: 495, Computed value: 500.0
We can see that the solution we provided is worse than the optimal solution by -5.0613 in total, and the difference in the solution values that we provided.
Test an infeasible solution¶
m = gp.read("misc07.mps")
sol = {m.getVarByName("COL260"): 2400.5}
sc = gma.SolCheck(m)
sc.test_sol(sol)
print(f"Solution Status: {sc.Status}")
Will print:
Read MPS format model from file misc07.mps
Reading time = 0.00 seconds
MISC07: 212 rows, 260 columns, 8619 nonzeros
Gurobi Optimizer version 11.0.3 build v11.0.3rc0
Thread count: 8 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 212 rows, 260 columns and 8619 nonzeros
Model fingerprint: 0xd79ad074
Variable types: 1 continuous, 259 integer (0 binary)
Coefficient statistics:
Matrix range [1e+00, 7e+02]
Objective range [1e+00, 1e+00]
Bounds range [1e+00, 2e+03]
RHS range [1e+00, 3e+02]
Presolve removed 0 rows and 7 columns
Presolve time: 0.00s
Explored 0 nodes (0 simplex iterations) in 1.72 seconds (0.00 work units)
Thread count was 1 (of 8 available processors)
Solution count 0
Model is infeasible
Best objective -, best bound -, gap -
Solution is infeasible for feasibility tolerance of 1e-06
Solution Status: 3
Here we can see that the solution we provided makes the problem infeasible. We
can use the SolCheck.inf_repair() function to repair the infeasibility.
sc.inf_repair()
for c in m.getConstrs():
if abs(c._Violation) > 0.0001:
print(f"{c.ConstrName}: RHS: {c.RHS}, Violation: {c._Violation}")
We get:
Relaxing to find smallest violation from fixed solution
Gurobi Optimizer version 11.0.3 build v11.0.3rc0
Thread count: 8 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 212 rows, 507 columns and 8866 nonzeros
Model fingerprint: 0x396303c6
Variable types: 248 continuous, 259 integer (0 binary)
Coefficient statistics:
Matrix range [1e+00, 7e+02]
Objective range [1e+00, 1e+00]
Bounds range [1e+00, 2e+03]
RHS range [1e+00, 3e+02]
Found heuristic solution: objective 2534.5000000
Presolve removed 0 rows and 7 columns
Presolve time: 0.01s
Presolved: 212 rows, 500 columns, 8823 nonzeros
Variable types: 70 continuous, 430 integer (380 binary)
Root relaxation: objective 0.000000e+00, 124 iterations, 0.00 seconds (0.00 work units)
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
0 0 0.00000 0 22 2534.50000 0.00000 100% - 0s
H 0 0 207.5000000 0.00000 100% - 0s
H 0 0 173.5000000 0.00000 100% - 0s
H 0 0 110.5000000 0.00000 100% - 0s
H 0 0 97.5000000 0.00000 100% - 0s
H 0 0 61.5000000 0.00000 100% - 0s
H 0 0 12.5000000 0.00000 100% - 0s
0 0 0.50000 0 25 12.50000 0.50000 96.0% - 0s
H 0 0 7.5000000 0.50000 93.3% - 0s
H 0 0 6.5000000 0.50000 92.3% - 0s
0 0 0.50000 0 34 6.50000 0.50000 92.3% - 0s
0 0 0.50000 0 31 6.50000 0.50000 92.3% - 0s
0 0 0.50000 0 30 6.50000 0.50000 92.3% - 0s
0 0 0.50000 0 27 6.50000 0.50000 92.3% - 0s
0 0 0.50000 0 23 6.50000 0.50000 92.3% - 0s
0 0 0.50000 0 27 6.50000 0.50000 92.3% - 0s
0 0 0.50000 0 22 6.50000 0.50000 92.3% - 0s
H 0 0 5.5000000 0.50000 90.9% - 0s
0 2 0.50000 0 22 5.50000 0.50000 90.9% - 0s
H 80 88 4.5000000 0.50000 88.9% 41.2 0s
H 132 209 3.5000000 0.50000 85.7% 37.7 0s
* 706 534 39 2.5000000 0.50000 80.0% 24.9 0s
H 1487 801 2.5000000 0.50000 80.0% 25.0 1s
H 1490 801 2.5000000 0.50000 80.0% 25.0 1s
5201 1713 0.58621 24 19 2.50000 0.50000 80.0% 24.9 5s
* 6181 908 26 1.5000000 0.50000 66.7% 24.6 5s
Cutting planes:
Gomory: 4
MIR: 8
Flow cover: 65
Explored 9399 nodes (234850 simplex iterations) in 6.19 seconds (6.17 work units)
Thread count was 8 (of 8 available processors)
Solution count 10: 1.5 2.5 2.5 ... 61.5
Optimal solution found (tolerance 1.00e-04)
Best objective 1.500000000000e+00, best bound 1.500000000000e+00, gap 0.0000%
Fixed values are 1.5 from a feasible solution
ROW001: RHS: 0.0, Violation: -0.5
ROW074: RHS: 1.0, Violation: 1.0
From this we can see that we would have to relax constraints ROW001 and
ROW074 by -0.5 and 1.0 to make the problem feasible.