# solvebilevel

solvebilevel is a built-in bilevel solver for problems with inner convex quadratic programs.

### Syntax

```
[sol,info] = solvebilevel(OConstr,OObj,IConstr,IObj,IVar,options)
```

### Examples

By default, the solver is based on a simple branching strategy, where branching is performed on complementarity of duals and slacks in the KKT conditions. Hence, the solver is only applicable to small academic examples. It has however been shown in experiments, that the solver performs fairly well in some cases compared to a big-M reformulation followed by a solution using a mixed-integer solver such as CPLEX or GUROBI. Since no big-M numbers are used, it is much more numerically robust in some cases where no reasonable bounds can be derived on dual variables.

```
sdpvar x1 x2 y1 y2 y3
OO = -8*x1-4*x2+4*y1-40*y2-4*y3;
CO = [x1>=0, x2>=0];
OI = x1+2*x2+y1+y2+2*y3;
CI = [[y1 y2 y3] >= 0,
-y1+y2+y3 <= 10,
2*x1-y1+2*y2-0.5*y3 <= 10,
2*x2+2*y1-y2-0.5*y3 <= 9.7];
solvebilevel(CO,OO,CI,OI,[y1 y2 y3])
>> value([y1 y2 y3])
ans =
0 6.0000 4.0000
>> value([x1 x2])
ans =
0 8.8500
```

As an alternative, we can tell the solver to derive the KKT conditions, and model the complementary slackness conditions using a big-M approach, and solve the problem using a standard mixed-integer solver.

```
ops = sdpsettings('bilvel.algorithm','external');
solvebilevel(CO,OO,CI,OI,[y1 y2 y3],ops)
```

If you would like to use this approach, you are however recommended to derive the problem by calling the KKT operator and setup the problem manually, in order to have full control of the way you work with bounds on the dual variables.

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