In every text I have come across that deals with minimizing the Euclidean distance (without constraints) in a 2D plane, the titular assumption seems to have been made. The standard procedure goes something like this: \begin{equation} ds^2 = dx^2 + dy^2 \Rightarrow s = \int_{x_0}^{x_1} \sqrt{1 + \left(\frac{dy}{dx}\right) ^2} dx \end{equation} Where we are expressing the Euclidean distance between two points $(x_0,y_0), (x_1,y_1)$ as an integral, and under the positive-definite assumption, taking the positive root only. Now the problem is to find a function that minimizes this integral, and the Euler-Lagrange equations are employed: \begin{equation} \frac{\partial f}{\partial y}(x, y, y_x) - \frac{d}{dx}\frac{\partial f}{\partial y_x}(x, y, y_x) = 0 \end{equation} Here is my question: how can we use this form of the equation for our integral? Are we not suggesting that we can completely parametrize all paths between $(x_0,y_0), (x_1,y_1)$ using $x$? If yes, can this be proven? Moreover, when the first term of this equation is said to yield to $0$, is this not implying that $dy/dx$ is independent of $y$? (This is clearly false for implicit functions.) I thought that the most general function between two points in a plane is of the form $h(x,y)=0$.
I suspect it is implied that every function of the form $h(x,y)=0$ can be wrriten as $y=f(x)$ or $x=g(y)$, albeit in non-closed form, but I am unsure.