  In this work, we study the computational complexity of reducing the squared
gradient magnitude for smooth minimax optimization problems. First, we present
algorithms with accelerated $\mathcal{O}(1/k^2)$ last-iterate rates, faster
than the existing $\mathcal{O}(1/k)$ or slower rates for extragradient, Popov,
and gradient descent with anchoring. The acceleration mechanism combines
extragradient steps with anchoring and is distinct from Nesterov's
acceleration. We then establish optimality of the $ \mathcal{O}(1/k^2)$ rate
through a matching lower bound.
