I’m having trouble formulating a model to minimize ||Ax - b||

Ideally I’m looking to minimize the 1 norm, but in trying to get there I ran into errors that I’m not sure I understand:

```
using JuMP
using ECOS
model = Model(with_optimizer(ECOS.Optimizer))
A = rand(10, 10)
b = rand(10)
@objective(model, Min, norm(A*x - b))
ERROR: JuMP no longer performs automatic transformation of `norm()` expressions into second-order cone constraints. They should now be expressed using the SecondOrderCone() set. For example, `@constraint(model, norm(x) <= t)` should now be written as `@constraint(model, [t; x] in SecondOrderCone())`
...
```

So I see that the `norm`

function has a problem- is this error correct even though I’m using it in the objective function, not a constraint?

When I try to make it into the norm squared, I get this:

```
@objective(model, Min, sum(el^2 for el in A*x - b))
ERROR: The solver does not support an objective function of type MathOptInterface.ScalarQuadraticFunction{Float64}.
Stacktrace:
...
```

Now it says the ECOS solver supports second-order conic programming (including problems with convex quadratic constraints and/or objective). I’m not super experienced with optimization yet, but wouldn’t ScalarQuadraticFunction fall under a quadratic objective?

Thanks