DAQP was supposedly solving convex mixed-binary quadratic programming until I tried.

The GitHub page

said that the matrix must be positive semi-definite, which it is. (The objective expression literally cannot be less than zero.)

```
using DAQP
using JuMP
function main()
model = Model(DAQP.Optimizer)
@variable(model, x, Bin)
@variable(model, y, Bin)
@objective(model,Min, (2*x+y)^2)
println(model)
optimize!(model)
solution_summary(model)
#println(value(x))
end
main()
```

This gave an infeasible result even though the problem is quite obviously a positive semi-definite matrix as required. It gave “infeasible” result, even though you could literally just have put any number in the system.

And if you do

```
using DAQP
using JuMP
function main()
model = Model(DAQP.Optimizer)
@variable(model, 0<=x<=3, Int)
@objective(model,Min, (x)^2)
println(model)
optimize!(model)
solution_summary(model)
#println(value(x))
end
main()
```

AssertionError: DAQP requires the objective to be strictly convex to support binary variables

Is this non-convex?

Maybe something is wrong here?