  In this paper we present a first-order method that admits near-optimal
convergence rates for convex/concave min-max problems while requiring a simple
and intuitive analysis. Similarly to the seminal work of Nemirovski and the
recent approach of Piliouras et al. in normal form games, our work is based on
the fact that the update rule of the Proximal Point method (PP) can be
approximated up to accuracy $\epsilon$ with only $O(\log 1/\epsilon)$
additional gradient-calls through the iterations of a contraction map. Then
combining the analysis of (PP) method with an error-propagation analysis we
establish that the resulting first order method, called Clairvoyant Extra
Gradient, admits near-optimal time-average convergence for general domains and
last-iterate convergence in the unconstrained case.
