If I have a matrix $G$ where I know that $G=(X^{T}X)^{-1}$, is there a way to find $X$? Specifically, I would like to find $G$ where $G$ is:
$$G = \begin{bmatrix} 0.125 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0.125 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0.375 & -0.125 & 0 & -0.25 \\ 0 & 0 & -0.125 & 0.375 & 0 & 0.25 \\ 0 & 0 & 0 & 0 & 0.125 & 0 \\ 0 & 0 & -0.25 & 0.25 & 0 & 0.5 \\ \end{bmatrix} $$
thanks!
The problem of recovering a valid $X$ given the value of $X^TX$ (which is basically the same as being given $G = (X^TX)^{-1}$) is numerically important.
First of all, note that there are multiple solutions, assuming any single solution exists. That is, we can't recover $X$ without additional assumptions. In particular: if $X^TX = A$, then we also have $(UX)^T(UX) = A$ for any orthogonal matrix $U$.
If we make the assumption that $X$ must be upper-triangular, then we do have a unique solution, assuming that $A = X^TX$ is invertible. The process of finding the upper-triangular $X$ that satisfies $X^TX = A$ (given a matrix $A$) is known as Cholesky decomposition.
On the other hand, if we make the assumption that $X$ must be symmetric-positive definite, then we once again get a unique solution. This $X$ is known as the "positive (semi)definite square root" of $X^TX$.