According to the docs on lowrankupdate!, I can compute the cholesky factor of `A + vv'`

in \mathcal{O}(n^2) time by updating an existing cholesky factor of `A`

.

How would this syntax work if I want the cholesky factor of A + e_i * e_j'? Here e_i and e_j are zero everywhere except it is 1 at the i or j th position. Clearly e_i * e_j' is a rank 1 matrix, but I don’t think it is possible to write e_i e_j' = vv' for some v right?

Actually, what I really need is update according to A + e_i * e_j' + e_j * e_i', i.e. a rank 2 update. Is this achievable?

A condition to have Cholesky decompositions is that the matrix is positive-definite which requires in particular: symmetric (real case) or Hermitian (complex case). The matrix A + e_i e_j^T would break this assumption.

However, adding A + e_i e_j^T + e_j e_i^T stays at least symmetric/Hermitian. (But might fail to be positive-definite.)

You could use three steps:

A + (e_i + e_j) (e_i + e_j)^T - e_i e_i^T - e_j e_j^T

That should correspond to one low-rank update and two downgrades, kind of like

```
a = randn(3,3)
A = a' * a
ei = [1.0, 0, 0]; ej = [0.0, 1, 0]
C = cholesky(A)
lowrankupdate!(C, ei + ej)
lowrankdowndate!(C, ei)
lowrankdowndate!(C, ej)
```

However, this will fail if a non-positive-definite matrix occurs as any of the results.

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Beautiful. Thank you so much.