A Symplectic Analysis of Alternating Mirror Descent

Motivated by understanding the behavior of the Alternating Mirror Descent (AMD) algorithm for bilinear zero-sum games, we study the discretization of continuous-time Hamiltonian flow via the symplectic Euler method. We provide a framework for analysis using results from Hamiltonian dynamics, Lie algebra, and symplectic numerical integrators, with an emphasis on the existence and properties of a conserved quantity, the modified Hamiltonian (MH), for the symplectic Euler method. We compute the MH in closed-form when the original Hamiltonian is a quadratic function, and show that it generally differs from the other conserved quantity known previously in that case. We derive new error bounds on the MH when truncated at orders in the stepsize in terms of the number of iterations, K, and use these bounds to show an improved O(K1/5) total regret bound and an O(K-4/5) duality gap of the average iterates for AMD. Finally, we propose a conjecture which, if true, would imply that the total regret for AMD scales as Oleft(Kvarepsilonright) and the duality gap of the average iterates as Oleft(K-1+varepsilonright) for any varepsilon>0, and we can take varepsilon=0 upon certain convergence conditions for the MH.