Consider a nonlinear system of the form $\boldsymbol{f}(\boldsymbol{x}) = \boldsymbol{0}_{\mathbb{R}^n}$ for $\boldsymbol{x} \in \mathbb{R}^n$, where the function $\boldsymbol{f}$ is given by \begin{equation} \boldsymbol{f}(\boldsymbol{x}) := \boldsymbol{g}(\boldsymbol{x}) + \boldsymbol{\underline{A}} \boldsymbol{x} + \boldsymbol{b} \in \mathbb{R}^n, \quad \boldsymbol{x} \in \mathbb{R}^n. \end{equation} Here, the function $\boldsymbol{g}$ is differentiable. Moreover, the functions $g_i$ depend only on $x_i$, i. e. $\frac{\partial g_i}{\partial x_j} \equiv 0$, $i \neq j$. The matrix $\boldsymbol{\underline{A}} \in \mathbb{R}^{n \times n}$ is large, sparse, non-symmetric, and invertible.
Can we take advantage of the purely diagonal nonlinearity in $\boldsymbol{f}$ somehow when solving the nonlinear system?
The assembly of the Jacobian matrix $\boldsymbol{\underline{J}_f}(\boldsymbol{x}) = \boldsymbol{\underline{J}_g}(\boldsymbol{x}) + \boldsymbol{\underline{A}}$ is cheap since $\boldsymbol{\underline{J}_g}(\boldsymbol{x})$ is a diagonal matrix. Therefore, Newton's method appears to be a natural choice, but other methods are also acceptable.
If we use Newton's method, then I guess my question boils down to whether we can take advantage of the special form of $\boldsymbol{\underline{J}_f}$ when computing the updates $\boldsymbol{\Delta x}$ in each Newton step, $\boldsymbol{\underline{J}_f}(\boldsymbol{x}_k) \boldsymbol{\Delta x} = - \boldsymbol{f}(\boldsymbol{x}_k)$?
