arXiv:2509.13582v3 Announce Type: replace Abstract: We investigate the continuous analogue of the Cholesky factorization, namely the pivoted Cholesky algorithm. Our analysis establishes quantitative convergence guarantees for kernels of minimal smoothness. We prove that for a symmetric positive definite Lipschitz continuous kernel $K:\Omega\times \Omega \rightarrow \mathbb{R}$ on a compact domain $\Omega\subset\mathbb{R}^d$, the residual of the Cholesky algorithm with complete pivoting (where the maximum value of the diagonal of the residual is selected as the next pivot) is uniformly bounded above by a constant multiple of the separation distance of the pivots, which implies the convergence rate $\|R_n\|_\infty=\mathcal{O}(n^{-1/d})$, where $R_n$ is the residual after $n$ Cholesky steps and $\|\cdot\|_\infty$ is the supremum norm. Along the way, we show that for any pivoting strategy, $\|R_n\|_\infty$ is bounded by a constant multiple of the fill distance of the pivots. Moreover, if $K$ is differentiable in both variables with a Lipschitz derivative and $\Omega$ is convex, our convergence rate improves to $\mathcal{O}(n^{-2/d})$. Our result closes a gap between theory and practice as previous analyses required $K$ to be $C^2$-regular or translation invariant to establish convergence, whereas empirical evidence indicated robust performance even for non-differentiable kernels. We further prove a connection between the Cholesky algorithm and the P-greedy interpolation method, as well as downstream applications to Gaussian process regression and matrix Cholesky factorization.