Are there any decompositions of a symmetric matrix that allow for the inversion of any submatrix? I am given a $J \times J$ symmetric matrix $\Sigma$. For a subset of $\{1, ..., J\}$,  $v$, let $\Sigma_{v}$ denote the associated square submatrix. I am in need of an efficient scheme for inverting $\Sigma_v$ for potentially any subset $v$. Essentially, I will have to invert many different submatricies $\Sigma_v$, but I won't know which ones I need to invert in advance of running some program; I would rather invest in a good matrix decomposition at the outset, if one exists (or otherwise get whatever information necessary, if not a decomposition).
I've messed around with the eigen decomposition a little bit, but wasn't able to coerce the inverse of a submatrix out of it.
UPDATE:
Apparently the term for the type of submatricies I want to invert is "principal submatrix". I'm wondering if I can't make some progress on this via the Cholesky decomposition. Suppose I'm content to calculate $\Sigma_{jj} ^ {-1}$ for any $j \in \{1, 2, ..., J\}$, where $\Sigma_{jj}$ denotes the submatrix obtained by deleting rows/columns greater than $j$. Write $\Sigma = LL^T$ and let $Q = L^{-1}$. Write 
$$
L = \begin{pmatrix}L_1 & \mathbf 0 \\ B & L_2\end{pmatrix}, 
Q = \begin{pmatrix}Q_1 & \mathbf 0 \\ C & Q_2\end{pmatrix} 
$$
where $L_1$ and $Q_1$ and $j \times j$. It follows that $\Sigma_{jj} = L_1 L_1^T$ and $Q_1 = L_1 ^{-1}$ so that $\Sigma_{jj} ^{-1} = Q_1^T Q_1$. So, once I have the Cholesky decomposition I have the inverses of the leading principal submatricies. This doesn't solve the problem as stated since I may need to deal with other principal submatricies, but it should be a useful partial solution.
 A: I imagine the best you can do is only one rank at a time building up or down to attain the desired sub-matrix portion. To reduce the dimension by one the simple formula is

$$\mathbf{A}^{-1} = E - \frac{\mathbf{f}\mathbf{g}^T}{h}$$

To see this, use the known inverse of the original and larger dimension matrix
$$\pmatrix{\mathbf A & \mathbf b\\ \mathbf c^T & d}^{-1} =  \pmatrix{\mathbf E & \mathbf f\\ \mathbf g^T & h}$$
to have
$$\pmatrix{\mathbf E & \mathbf f\\ \mathbf g^T & h}\pmatrix{\mathbf A & \mathbf b\\ \mathbf c^T & d}  = \pmatrix{ \mathbf I & \mathbf 0 \\ \mathbf 0^T & 1}$$
Now to find the quantity $A^{-1}$, simply left multiply the equation with
$$\pmatrix{\mathbf{I} & -\mathbf{f}\frac{1}{h} \\ \mathbf{0}^T & 1}$$
giving
$$ \pmatrix{\mathbf{E}- \mathbf{f}\frac{1}{h}\mathbf{g}^T & \mathbf{0} \\ \mathbf{g}^T & h}\pmatrix{\mathbf{A} & \mathbf{b} \\ \mathbf{c}^T & d} = \pmatrix{\mathbf{I} & -\mathbf{f}\frac{1}{h} \\ \mathbf{0}^T & 1}$$
The upper left portion of this matrix equation is
$$\left( \mathbf{E} - \mathbf{f}\frac{1}{h}\mathbf{g}^T\right)\mathbf{A} = \mathbf{I}$$
and shows the formula.
To go the other direction (adding a row and column) you can use what is called the bordering method as described in this answer
A: If we note $\Sigma_j = \Sigma_{[1..j-1]\cup[j+1..J]}$ the matrix $\Sigma$ with deletion of the $j$th row and column, the formulae for computing the updated Cholesky decomposition are :
$$\Sigma = \begin{bmatrix} A_{11}&A_{12}&A_{13}\\A_{12}^T&A_{22}&A_{23}\\A_{13}^T&A_{23}^T&A_{33} \end{bmatrix} $$
$$L = \begin{bmatrix}
L_{11}&L_{12}&L_{13}\\0&L_{22}&L_{23}\\0&0&L_{33} \end{bmatrix} $$
$$\Sigma_j = \begin{bmatrix} A_{11}&A_{13}\\A_{13}^T&A_{33} \end{bmatrix} $$
$$L_j = \begin{bmatrix}
L_{11}&L_{13}\\0&\mathsf{cholupdate}(L_{33},L_{23})\end{bmatrix} $$
where $\mathsf{cholupdate}(A,B) = \mathsf{chol}(A^TA+B^TB)$. These computations perform in $O(n^2)$, so if you have $K$ deletions, the computation cost is $O(Kn^2)$.
A: The solution adam W proposed generalizes to rank-$n$ updates; it is not necessary to arrive by repeated rank-1 updates. In his derivation, we can consider $h$ a matrix instead of a scalar, and replace the bottom-right-hand 1 with an identity matrix and $\frac{1}{h}$ with $h^{-1}$. It still works.
