# Minimum ellipses

This example is from the Boyd & Vandenberghe book "Convex Optimization". Given a set of ellipses centered on the origin

`E(A) = { u | u^T inv(A) u <= 1 }`

find a "minimal" ellipse that contains the provided ellipses.

We can formulate this as an SDP:

```
minimize trace(WX)
subject to X >= A_i, i = 1,...,m
X PSD
```

where W is a PD matrix of weights to choose between different solutions.

```
using JuMP
import LinearAlgebra
import SCS
import Test
function example_min_ellipse()
# We will use three ellipses: two "simple" ones, and a random one.
As = [
[2.0 0.0; 0.0 1.0],
[1.0 0.0; 0.0 3.0],
[2.86715 1.60645; 1.60645 1.12639]
]
# We change the weights to see different solutions, if they exist
weights = [1.0 0.0; 0.0 1.0]
model = Model(SCS.Optimizer)
set_silent(model)
@variable(model, X[i=1:2, j=1:2], PSD)
@objective(model, Min, LinearAlgebra.tr(weights * X))
for As_i in As
@SDconstraint(model, X >= As_i)
end
optimize!(model)
Test.@test termination_status(model) == MOI.OPTIMAL
Test.@test primal_status(model) == MOI.FEASIBLE_POINT
Test.@test objective_value(model) ≈ 6.46233 atol = 1e-5
Test.@test value.(X) ≈ [3.1651 0.8022; 0.8022 3.2972] atol = 1e-4
return
end
example_min_ellipse()
```

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