Does $(\mathbf A+\epsilon \mathbf I)^{-1}$ always exist, given that $\mathbf A$ is a square and positive (and possibly singular) matrix and $\epsilon$ is a small positive number? I want to use this to regularize a sample covariance matrix ($\mathbf A = \Sigma$) in practice, so that I can compute the inverse, which I need to calculate a Mahalanobis distance between two samples. In practice, my covariance matrix is often singular. I know the term $(\mathbf A+\epsilon \mathbf I)^{-1}$ often appears in the context of least squares problems involving Tikhonov regularization (ridge regression). However, I've never seen a statement, proof, or reference which says that the expression is always invertible.
Can any of you help me with a proof or reference?
Recall that $\det(A-t I)=p_A(t)$ is the characteristic polynomial of $A$, which is a degree $N$ polynomial in $t$, where $N$ is the number of rows of $A$. Hence, it is a continuous function in $t$, and has at most $N$ roots, so there is some largest negative root $t_0$, or possibly no negative roots at all (in that case, choose $t_0=-1$ just for completeness). For all $0 < \epsilon < - t_0$, $p_A(-\epsilon)$ is nonzero, meaning that $\det(A+ \epsilon I) \ne 0$, so $A+\epsilon I$ is invertible.
EDIT: Corrected signs to agree with the question