Find diagonal of inverse matrix

I have computed the Cholesky of a positive semidefinite 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 eigenvalues 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?

I've seen that random projections does wonders for inverting matrices. Could something like this be applied here?

• I don't see why this question should be on hold. The subtext of the question seems to be clear and constitutes a non-trivial question: what is an optimal way to compute the diagonal elements of a symmetric positive semi-definite matrix? The naive way to compute the entire inverse is $O(n^3)$. But can one get just the diagonal with a smaller asymptotic exponent? Oct 16, 2014 at 20:48
• Not a solution of your problem. However Schur complement formula tells that $\frac{1}{(\Theta^{-1})_{ii}}=\Theta_{ii}-r_{i}\tilde\Theta^{-1}c_{i}$, where $r_{i}=(\Theta_{i1},\ldots,\hat\Theta_{ii},\ldots,\Theta_{in})$, $c_{i}=(\Theta_{1i},\ldots,\hat\Theta_{ii},\ldots,\Theta_{ni})$ ($\hat a$ means $a$ is removed), and $\tilde\Theta$ is obtained from $\Theta$ after removing $i$th row and $i$th Column. Nov 2, 2014 at 18:45
• The following question seems related: math.stackexchange.com/questions/64420/… Dec 4, 2017 at 20:51

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 $${\bf B} \succ 0$$.

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.

$$\textbf{d}^*(\textbf{B}) = \operatorname{argmin}_{\textbf{d}} \tfrac 1 2 \| \textbf{B} \operatorname{diag} (\textbf{d}) - \textbf{I} \|_F^2$$

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.

• This is well-known and is called "sparse approximate inverse". These are generally used as preconditioners.
– max
Mar 7, 2018 at 16:26
• So not 100 years old, but more like 20 or so. Thanks for commenting with the correct terminology. Mar 7, 2018 at 19:42
• What if $B$ is not posdef, not even symmetric: $\|b_i\|^2 \to \|b_{row\ i}\| \|b_{col\ i}\|$ ? Would you have a test case or two ? May 15, 2020 at 16:19
• @denis , this was to cheaply approximate the inverse of a Gram matrix. So B was always pos semidef. Adding epsilon to the diagonal elements makes it definite in practice. May 15, 2020 at 20:10

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.

• I guess that a more interesting answer would be a way to compute the just diagonal of the inverse in less than $O(n^3)$ time, is the same order as the time it takes to compute the entire inverse. Oct 16, 2014 at 13:08
• Unfortunately I'm doing all of this in Matlab. So doing for loops is worse than doing the actual inverse. But +1 regardless for the answer. Oct 17, 2014 at 1:51
• @IgorKhavkine but forward and backward substitutions are both $O(n^2)$? Jun 25, 2018 at 21:00
• @BenjaminChristoffersen but you have to solve with $n$ different right hand side vectors, so altogether it's $n^3$. Jun 26, 2018 at 1:32
• @DavidKetcheson Yes, sorry. Jun 26, 2018 at 19:28

Tang and Saad have a method that uses random vectors (not necessarily projections):

A Probing Method for Computing the Diagonal of the Matrix Inverse

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 $O(n^2)$.