(In)approximability of Maximum Minimal FVS

We study the approximability of the NP-complete \textsc{Maximum Minimal Feedback Vertex Set} problem. Informally, this natural problem seems to lie in an intermediate space between two more well-studied problems of this type: \textsc{Maximum Minimal Vertex Cover}, for which the best achievable approximation ratio is $\sqrt{n}$, and \textsc{Upper Dominating Set}, which does not admit any $n^{{1-\epsilon}$} approximation. We confirm and quantify this intuition by showing the first non-trivial polynomial time approximation for \textsc{Max Min FVS} with a ratio of $O(n^{2/3})$, as well as a matching hardness of approximation bound of $n^{{2/3-\epsilon}$,} improving the previous known hardness of $n^{{1/2-\epsilon}$.} The approximation algorithm also gives a cubic kernel when parameterized by the solution size. Along the way, we also obtain an $O(\Delta)$-approximation and show that this is asymptotically best possible, and we improve the bound for which the problem is NP-hard from $\Delta\ge 9$ to $\Delta\ge 6$. Having settled the problem's approximability in polynomial time, we move to the context of super-polynomial time. We devise a generalization of our approximation algorithm which, for any desired approximation ratio $r$, produces an $r$-approximate solution in time $n^{{O(n/r{3/2})}$.} This time-approximation trade-off is essentially tight: we show that under the ETH, for any ratio $r$ and $\epsilon>0$, no algorithm can $r$-approximate this problem in time $n^{{O((n/r{3/2}){1-\epsilon})}$,} hence we precisely characterize the approximability of the problem for the whole spectrum between polynomial and sub-exponential time, up to an arbitrarily small constant in the second exponent.