Hi. I can’t seem to find a definitive answer so I’m posting here for help. What is the standard and latest way in JuMP to construct an objective function for a Frobenius norm?

Say I have a (fictional) minimization of

where \mathbf{X}\in\mathbb{R}^{n\times K}, K > n, is some data matrix. And, the matrices \mathbf{A}\in\mathbb{R}^{n\times n}, \mathbf{Q}=\mathbf{Q}^\top\in\mathbb{R}^{n\times n} are given. Also assume \mathbf{P} = \mathbf{P}^\top.

Would the code to solve this be like the following?

```
n = 5
K = 100
X = rand(n, K)
A = rand(5,5)
Q = rand(5,5)
Q *= Q'
model = Model(Ipopt.Optimizer)
@variable(model, P[1:n, 1:n], Symmetric)
@objective(model, sum((P*A*X - Q*X).^2))
optimize!(model)
```

Or do I have to use the `MOI.SecondOrderCone()`

as in the example below (for a vector case)?

```
@constraint(model, [t; x] in SecondOrderCone())
```