Suppose to have a $n \times n$ positive definite matrix $\boldsymbol{\Sigma}$ and let $ \boldsymbol{\Sigma}= \mathbf{B}\mathbf{B}^T$ where $\mathbf{B}$ is obtained with the Cholesky decomposition.
Let $\mathbf{S} = diag(s_1,s_2,\dots , s_n)$ be a diagonal matrix where each $s_i, i=1,\dots, n$ can assume only value $1$ or $-1$ and let $\mathcal{S}$ be the space of all possible values of $\mathbf{S}$.
Consider the matrix $\boldsymbol{\Sigma}_s = \mathbf{S} \boldsymbol{\Sigma} \mathbf{S} $ and its cholesky decomposition $\boldsymbol{\Sigma}_s= \mathbf{B}_s\mathbf{B}_s^T$.
Let $\mathbf{1}_n$ be a vector of n 1s. I am wondering if there is a easy way to compute
$\sum_{\mathbf{S} \in \mathcal{S}}\mathbf{B}_s \mathbf{1}_n$
EDIT 1
I compute the sum for a m $\times$ m matrix, with m=1,2,3, and if we let $\mathbf{D}$ be the vector of the diagonal element of $\mathbf{B}$, then $\sum_{\mathbf{S} \in \mathcal{S}}\mathbf{B}_s \mathbf{1}_m = 2^m \mathbf{D}$.
EDIT 2
I forgot to say that $\boldsymbol{\Sigma}$ is a covariance matrix
I think i found the solution
We can write the matrix:
$\boldsymbol{\Sigma}_s = \mathbf{B}_s \mathbf{B}^t = \mathbf{S}\boldsymbol{\Sigma} \mathbf{S} = \mathbf{S}\mathbf{B}\mathbf{B}^t\mathbf{S}$
we can also write $\mathbf{S}\mathbf{B}\mathbf{B}^t\mathbf{S} = \mathbf{S}\mathbf{B}\mathbf{S}\mathbf{S}\mathbf{B}^t\mathbf{S} = \mathbf{B}_s \mathbf{B}^t$.
Then we have $\mathbf{B}_s = \mathbf{S}\mathbf{B}\mathbf{S}$. This is a valid Cholesky decomposition since $ \mathbf{S}\mathbf{B}\mathbf{S}$ is lower triangular and has all positive values in the diagonal.
Then $\sum_{\mathbf{S} \in \mathcal{S}} \mathbf{S}\mathbf{B}\mathbf{S} = 2^n diag(\mathbf{B}) $ (with diag() i mean a diagonal matrix with diagonal entries given by the diagonal of the argument) since each non diagonal elements of $\mathbf{S}\mathbf{B}\mathbf{S}$ is summed half the time with a positive sign and the other half with negative.
Is it true? can someone check if this is really the answer?