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This study develops a fixed-time convergent saddle point dynamical system forsolving min-max problems under a relaxation of standard convexity-concavityassumption. In particular, it is shown that by leveraging the dynamical systemsviewpoint of an optimization algorithm, accelerated convergence to a saddlepoint can be obtained. Instead of requiring the objective function to bestrongly-convex--strongly-concave (as necessitated for accelerated convergenceof several saddle-point algorithms), uniform fixed-time convergence isguaranteed for functions satisfying only the two-sided Polyak-{\L}ojasiewicz(PL) inequality. A large number of practical problems, including the robustleast squares estimation, are known to satisfy the two-sided PL inequality. Theproposed method achieves arbitrarily fast convergence compared to any otherstate-of-the-art method with linear or even super-linear convergence, as alsocorroborated in numerical case studies.

Fixed-Time Convergence for a Class of Nonconvex-Nonconcave Min-Max Problems