I know that if $f$ is a quasi-convex function in one dimension (that is, $f: \mathbb{R} \to \mathbb{R}$), then we can use the 'golden section' line search to find the optimizer.
Now suppose I have a function $f: \mathbb{R}^2 \to \mathbb{R}$ which is quasi-convex. I seek to minimize $f$. We can assume that we are initially given a bounded axis-aligned rectangle which is guaranteed to contain the minimizer.
Is there an analogy to line search in 2D which I can use to minimize this function?