We show the strong-convexity assumption of regularization-based methods for solving bilinear saddle point problems may be relaxed to a weaker notion of area-convexity with respect to an alternating bilinear form. This allows bypassing the infamous '_' barrier for strongly convex regularizers that has stalled progress on a number of algorithmic problems.

Applying area-convex regularization, we present a nearly-linear time approximation algorithm for solving matrix inequality systems A X ≤ B over right-stochastic matrices X. By combining that algorithm with existing work on preconditioning maximum-flow, we obtain a nearly-linear time approximation algorithm for maximum concurrent flow in undirected graphs: given an undirected, capacitated graph with m edges and k demand vectors, the algorithm takes Õ(mkε^'1) time and outputs k flows routing the specified demands with total congestion at most (1+ε) times optimal.