  This study develops a fixed-time convergent saddle point dynamical system for
solving min-max problems under a relaxation of standard convexity-concavity
assumption. In particular, it is shown that by leveraging the dynamical systems
viewpoint of an optimization algorithm, accelerated convergence to a saddle
point can be obtained. Instead of requiring the objective function to be
strongly-convex--strongly-concave (as necessitated for accelerated convergence
of several saddle-point algorithms), uniform fixed-time convergence is
guaranteed for functions satisfying only the two-sided Polyak-{\L}ojasiewicz
(PL) inequality. A large number of practical problems, including the robust
least squares estimation, are known to satisfy the two-sided PL inequality. The
proposed method achieves arbitrarily fast convergence compared to any other
state-of-the-art method with linear or even super-linear convergence, as also
corroborated in numerical case studies.
