  In this work, we consider a nonsmooth minimisation problem in which the
objective function can be represented as the maximum of finitely many smooth
``subfunctions''. First, we study a smooth min-max reformulation of the
problem. Due to this smoothness, the model provides enhanced capability of
exploiting the structure of the problem, when compared to methods that attempt
to tackle the nonsmooth problem directly. Then, we present several approaches
to identify the set of active subfunctions at a minimiser, all within finitely
many iterations of a first order method for solving the smooth model. As is
well known, the problem can be equivalently rewritten in terms of these
subfunctions, but a key challenge is to identify this set \textit{a priori}.
Such an identification is clearly beneficial in an algorithmic sense, since one
can apply this knowledge to create an equivalent problem with lower complexity,
thus facilitating generally faster convergence. Finally, numerical results
comparing the accuracy of each of these approaches are presented, along with
the effect they have on reducing the complexity of the original problem.
