  This work introduces a moving anchor acceleration technique to extragradient
algorithms for smooth structured minimax problems. The moving anchor is
introduced as a generalization of the original algorithmic anchoring framework,
i.e. the EAG method introduced in [32], in hope of further acceleration. We
show that the optimal order of convergence in terms of worst-case complexity on
the squared gradient, O(1/k2), is achieved by our new method (where k is the
number of iterations). We have also extended our algorithm to a more general
nonconvex-nonconcave class of saddle point problems using the framework of
[14], which slightly generalizes [32]. We obtain similar order-optimal
complexity results in this extended case. In both problem settings, numerical
results illustrate the efficacy of our moving anchor algorithm variants, in
particular by attaining the theoretical optimal convergence rate for first
order methods, as well as suggesting a better optimized constant in the big O
notation which surpasses the traditional fixed anchor methods in many cases. A
proximal-point preconditioned version of our algorithms is also introduced and
analyzed to match optimal theoretical convergence rates.
