I’m exploring the following polynomial optimization problem:

```
using SumOfSquares
using DynamicPolynomials
using MosekTools
# Create symbolic variables (not JuMP decision variables)
@polyvar x1 x2
# Create a Sum of Squares JuMP model with the Mosek solver
model = SOSModel(with_optimizer(Mosek.Optimizer))
# Create a JuMP decision variable for the lower bound
@variable(model, γ)
@variable(model, x)
# f(x) is the Goldstein-Price function
f1 = x1+x2+1
f2 = 19-14*x1+3*x1^2-14*x2+6*x1*x2+3*x2^2
f3 = 2*x1-3*x2
f4 = 18-32*x1+12*x1^2+48*x2-36*x1*x2+27*x2^2
f = (1+f1^2*f2)*(30+f3^2*f4)
```

when the constraint takes the form of

```
@constraint(model, f >= γ)
```

this problem can be solved to optimal:

```
@objective(model, Max, γ)
optimize!(model)
# The lower bound found is 3
println(objective_value(model))
```

However, when I added a redundant constraint

```
@constraint(model, f1>= x)
```

the output indicates that this problem has not been solved:

```
Problem
Name :
Objective sense : max
Type : CONIC (conic optimization problem)
Constraints : 49
Cones : 0
Scalar variables : 3
Matrix variables : 1
Integer variables : 0
Optimizer started.
Presolve started.
Eliminator - tries : 0 time : 0.00
Lin. dep. - tries : 0 time : 0.00
Lin. dep. - number : 0
Presolve terminated. Time: 0.00
Optimizer terminated. Time: 0.00
0.0
```

I’m pretty confused about this result since the additional constraint is obviously redundant. Why adding a redundant ruins everything? Thanks for your help in advance!