Given 9 values for $f(x,y)$ where $(x,y) \in \{-1,0,+1\}^2$ I wish to fit a paraboloid surface $z = f(x,y)$ to these 9 values. Once I have $f$ I want to locate its minimum value. The center value $f(0,0)$ is known to be the smallest of the 9 input values so there exists a minimum value of $f$ on the domain $[-1,+1]^2.$ I wish find $(x^*,y^*) = \mathrm{argmin}\ f(x,y).$
I could find 6 the coefficients for \begin{equation} f(x,y) = ax^2 + by^2 + cxy + dx + ey + g \end{equation} using a least squared error fit. To find the minimum I simply solve $f_x = 0$ and $f_y = 0$ which leads to a simple linear system which has a unique solution.
Conversely I could find the 9 coefficients for \begin{equation} f(x,y) = ax^2y^2 + bx^2y + cxy^2 + dx^2 + ey^2 + gxy + hx + iy + j \end{equation} and get an exact fit (9 linear equations and 9 unknowns). Finding the minimum of this function would require something akin to gradient descent.
The problem I am a trying to solve comes from this paper where it cryptically says "This produces values that are integers; to get a subpixel estimate, we fit a parabola to the 3×3 pixels centered at $(u_0,v_0)$ and extract its minimum".
The problem is I don't have a good feel for what the second surface is. Clearly if I hold either $x$ or $y$ constant then I end up with a 2D parabola. But this doesn't necessarily have a unique extrema point? Or does it?
Any insight on how I should be finding the minimum of a parabola fitted to 9 points?