Is it possible to compute a (strictly) triangular Schur decomposition for a matrix with complex eigenvalues?

With the code below I create such a matrix:

```
T = rand(2,2)
λ = rand() + rand()*im
D = [real(λ) imag(λ); -imag(λ) real(λ)]
A = T*D/T
```

`schur`

then returns a quasitriangular decomposition. In my case of a 2x2 matrix with complex eigenvalues:

```
S,U,Λ = schur(A)
```

```
Schur{Float64,Array{Float64,2}}
T factor:
2×2 Array{Float64,2}:
0.921219 -0.0444826
4.41202 0.921219
Z factor:
2×2 Array{Float64,2}:
0.774798 0.632209
-0.632209 0.774798
eigenvalues:
2-element Array{Complex{Float64},1}:
0.9212193623156234 + 0.44301030411320136im
0.9212193623156234 - 0.44301030411320136im
```