I am looking for an answer to the following question:
Is the objective function $\max \min(f(x))$ convex for all $f(x)$? Is it convex for a function like the following? $$f(x) = \sum_{i=1}^{\infty} x_i$$
I don't really know how to work on the max min (or min max) ... I know max / min are convex but does it help to understand the convexity of max min (min max) ?
Example: Let's say we would like to minimize the maximum energy use for a bunch of jobs over the time, and be sure that every jobs are finished on time (typical scheduling problem).
We are going to have a problem like that: $min~max(f(x))$, where $f(x) = E_t^J = \sum_{j=1}^{J}x_{j},\forall j \in J$ and $\forall~t \in T$. Where $J$ is the set of our jobs and $x_j$ the energy you deliver to each job working in a timeslot $t$ and $E_t^J$ the total energy consumes by the jobs working in timeslot $t$.
We would do the maximisation over $t$ and the minimization over $J$. How can we state if this is convex or not ?