Settings: Julia 1.8, JuMP 1.13.0, Gurobi.jl 1.0.2

I have been trying to check which type of cuts that I included through callbacks are still active.

- As shown in Gurobi’s log, the initial formulation has 9010 constraints.
- During the branch-and-bound, I separate 2 types of constraints on fractional solutions and, when a violation is found, I keep track of how many cuts I applied for each one.
- For this instance, 1 cut of type “A” and 6 of type “B” were added but only one is binding in the optimal solution as per Gurobi’s log.

```
Optimize a model with 9010 rows, 1118 columns and 33885 nonzeros
Model fingerprint: 0x0d75667f
Variable types: 94 continuous, 1024 integer (1024 binary)
Coefficient statistics:
Matrix range [2e-02, 1e+03]
Objective range [1e+00, 1e+04]
Bounds range [0e+00, 0e+00]
RHS range [5e-14, 1e+03]
Presolve removed 8335 rows and 872 columns
Presolve time: 0.02s
Presolved: 675 rows, 246 columns, 3559 nonzeros
Variable types: 77 continuous, 169 integer (169 binary)
Found heuristic solution: objective 50789.110000
Root relaxation: objective 2.054028e+04, 137 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 20540.2850 0 16 50789.1100 20540.2850 59.6% - 0s
0 0 20542.8500 0 - 50789.1100 20542.8500 59.6% - 1s
0 0 20543.7700 0 14 50789.1100 20543.7700 59.6% - 1s
H 0 0 40702.450000 20543.7700 49.5% - 2s
H 0 0 30587.930000 20543.7700 32.8% - 2s
0 2 20545.5950 0 6 30587.9300 20545.5950 32.8% - 2s
* 19 9 4 20577.570000 20549.0600 0.14% 16.6 2s
* 23 0 5 20575.760000 20549.7150 0.13% 16.2 2s
Cutting planes:
User: 1
Lazy constraints: 26
Explored 28 nodes (614 simplex iterations) in 2.61 seconds (0.05 work units)
Thread count was 8 (of 8 available processors)
Solution count 5: 20575.8 20577.6 30587.9 ... 50789.1
Optimal solution found (tolerance 1.00e-07)
Best objective 2.057576000000e+04, best bound 2.057576000000e+04, gap 0.0000%
```

To check the constraints slack, I do as suggested in this doc.

```
function constraint_report(c::ConstraintRef)
return (
value = value(c),
rhs = normalized_rhs(c),
slack = normalized_rhs(c) - value(c),
)
end
constraint_df = DataFrames.DataFrame(
constraint_report(ci) for (F, S) in list_of_constraint_types(model) for
ci in all_constraints(model, F, S) if F == AffExpr
)
display(constraint_df)
```

However, it shows just the 9010 initial constraints, i.e., it doesn’t included the added cut.

```
9010×3 DataFrame
Row│ value rhs slack
│ Float64 Float64 Float64
──────────────────────────────────────
1 │ 1.0 1.0 0.0
2 │ 1.0 1.0 0.0
3 │ 1.0 1.0 0.0
4 │ 1.0 1.0 0.0
5 │ 1.0 1.0 0.0
6 │ 1.0 1.0 0.0
7 │ 1.0 1.0 0.0
8 │ 1.0 1.0 0.0
9 │ 1.0 1.0 0.0
10 │ 1.0 1.0 0.0
11 │ 1.0 1.0 0.0
12 │ 1.0 1.0 0.0
13 │ 1.0 1.0 0.0
14 │ 1.0 1.0 0.0
15 │ 1.0 1.0 0.0
16 │ 1.0 1.0 0.0
17 │ 1.0 1.0 1.11022e-16
18 │ 1.0 1.0 0.0
⋮ │ ⋮ ⋮ ⋮
8993 │ 0.0 1.0 1.0
8994 │ 0.0 1.0 1.0
8995 │ 0.0 1.0 1.0
8996 │ 0.0 1.0 1.0
8997 │ 0.0 1.0 1.0
8998 │ 0.0 1.0 1.0
8999 │ 0.0 1.0 1.0
9000 │ 0.0 1.0 1.0
9001 │ 0.0 1.0 1.0
9002 │ 0.0 1.0 1.0
9003 │ 0.0 1.0 1.0
9004 │ 0.0 1.0 1.0
9005 │ 0.0 1.0 1.0
9006 │ 0.0 1.0 1.0
9007 │ 0.0 1.0 1.0
9008 │ 0.0 1.0 1.0
9009 │ 1.0 1.0 0.0
9010 │ 0.0 1.0 1.0
8974 rows omitted
```

I wonder if there is a way of retrieving which added UserCut is binding in the optimal solution.

Let me know if more details are needed. Thanks in advance.