A convex subset $C$ of $\mathbb{R}^{n^2}$ is given as the set of positive semidefinite $n\times n$ matrices whose coefficients fulfill some affine equations.
Now, if you want to minimize a linear function on $C$, you're dealing with semidefinite programming, and that's efficiently solvable. But what if you want to estimate the diameter of $C$? I can very well imagine that this is a difficult task, but can find no helpful reference (and I'm more willing to blame that on me not finding the right keywords than on the research not having been already done). I would consider estimating the diameter only up to some factor, large but at most slowly increasing in the dimension; so I'm interested in general in the hardness of approximating the diameter.