Newtons Method in 2D I need to learn how to use Newtons Method in the 2nd dimension for a research report, but have had a hard time finding any information on the topic that is not in python code. 
I have found the equation: $$x_{n+1}=x_n-J^{-1}f(x_n)$$
I do not really know where to go from here though. I wanted to know where the equation came from, how to use it, why the inverse Jacobian is used, and any other useful information that can be explained to me. Any help would be greatly appreciated. 
 A: There are two different (but related) Newton's methods: one for solving nonlinear equations, and one for optimization.  Newton's method for optimization is explained here.
Here's a quick explanation of Newton's method for solving $f(x) = 0$, where $f:\mathbb R^N \to \mathbb R^N$ is a differentiable function.  Given our current estimate $x_n$ of a solution, ideally we would like to find $\Delta x$ such that
$f(x_n + \Delta x) = 0$.  However, rather than solving this condition exactly (which is likely too difficult), we instead use the approximation
$f(x_n + \Delta x) \approx f(x_n) + f'(x_n) \Delta x$, and we find $\Delta x$ such that
$f(x_n) + f'(x_n) \Delta x = 0$.  In other words,
we take $\Delta x = -\ f'(x_n)^{-1} f(x_n)$.
We can hope that $x_{n+1} = x_n + \Delta x$ is improvement upon $x_n$.
(We have to worry that $f'(x_n)$ might not be invertible, but it can be shown that this won't happen if we start the iteration sufficiently close to a vector $x^*$ such that $f(x^*) = 0$ and $f'(x^*)$ is invertible.  I would have to check exactly what conditions are assumed in a convergence proof.)
By the way, Newton's method for optimization minimizes a twice-differentiable function $f$ simply by solving the equation $\nabla f(x) = 0$ using Newton's method for nonlinear systems.
