Let $f(x) = -g(x)\cos(2 \pi h(x))$, where $h(x)$ and $g(x)$ are both continuous and invertible functions.
Let the "attraction basin" of a minima of $f(x)$ be defined as the set of points which lead to that minima when gradient descent is performed. Basically, the region around the minima where if you "let a ball go", it would "roll down" to the minima. (Not sure how better to explain it, if this is confusing or ambiguous let me know).
Finally, let $L(f(x))$ be a function that returns the local minima of the attraction basin of x.
For example, if $f(x) = -\cos(2\pi x)$, then minimas occur at integer coordinates so $L(f(x)) = round(x)$.
If $x = 0.4$, then $L(f(0.4)) = round(0.4) = 0$. If you performed gradient descent at 0.4, you would reach 0.
I am trying to figure out how to make this work for $f(x) = -g(x)\cos(2\pi h(x))$.
Without the $g(x)$, it is simple and $L(f(x)) = h^{-1}(round(h(x)))$, because $h(x)$ is invertible.
However, with the $g(x)$, I am stumped. How can I figure out $L(f(x))$ for that? In other words, how can I predict where the minimas and their attraction basins are?
I know that $f'(x) = 2\pi g(x) h'(x) \sin(2\pi h(x)) - g'(x) \cos(2\pi h(x))$, and the minimas come when $f'(x) = 0$. What are necessary conditions on $g(x)$ such that we can we predict the minimas of $f(x)$?
Any help is very, very much appreciated.
Thanks!