Find diagonal of inverse matrix I have computed the Cholesky of a positive semidifinite matrix $\Theta$. However I wish to know the diagonal elements of the inverse of $\Theta^{-1}_{ii}$. Is it possible to do this using the cholesky that I have computed? Or will finding the Eigen values alone (without the orthonormal matrices of a SVD) help this cause.
Are there any other suggestions or alternative decompositions that will aid finding the inverse matrix diagonal?
Edit: I've seen that random projections does wonders for inverting matrices. Could something like this be applied here?
 A: I stumbled onto this question when trying to answer a similar question 
I want a diagonal matrix that best approximates the inverse of a matrix $\textbf{B} \succ 0$.
I'll post my answer to that question in case it helps other (and maybe OP).  In this case, "best" means nearest in the $\ell_2$ sense.
$$\textbf{d}^*(\textbf{B}) = {argmin}_{\textbf{d}} \tfrac 1 2 \| \textbf{B} diag(\textbf{d}) - \textbf{I} \|_F^2$$
This is separable in $d_i$ and differentiable.  Setting the gradient to zero brings us to the closed form (and very cheap) solution
$[\textbf{d}^*]_i = \frac {b_{ii} } { \| \textbf{b}_i \|^2}$
(Note in complex numbers, you'd need to conjugate)
I wouldn't be surprised if this has been known for 100 years, but I couldn't easily find it.
A: If you have the Cholesky decomposition, you can easily compute the whole matrix inverse.  Since
$$\Theta = R^* R$$
where $R$ is upper-triangular, then you can find $\Theta^{-1}$ by solving
$$R^* R X = I$$
where $I$ is the identity.  The latter system can be solved by forward and backward substitution.
If you only want the diagonal entries of $X$, you could save perhaps half the computation by stopping the backward substitution process (for each column) when you get to the diagonal entry.
A: Tang and Saad have a method that uses random vectors (not necessarily projections): 
A Probing Method for Computing the Diagonal of the Matrix Inverse
A: I think that we cannot have a complexity $<n^3/3$. Indeed let $\Theta=LL^*$ where $L$ is lower triangular. Clearly $\Theta^{-1}={L^*}^{-1}L^{-1}$ ; let $L^{-1}=[u_{i,j}]$. Then (F)  ${\Theta^{-1}}_{i,i}=\sum_{j\geq i}|u_{j,i}|^2$. Then we must know the $(|u_{j,i}|)$ and (in my opinion) we must know the $(u_{j,i})$, that is $L^{-1}$. The complexity of the calculation of $L^{-1}$ is $\sim n^3/3$ (cf. A Fast Triangular Matrix Inversion by R. Mahfoudhi). Along the second step, the calculation of the $({\Theta^{-1}}_{i,i})$ (with (F)) is in $O(n^2)$.
