  This work addresses distributed optimization, where a network of agents wants
to minimize a global strongly convex objective function. The global function
can be written as a sum of local convex functions, each of which is associated
with an agent. We propose a continuous-time distributed mirror descent
algorithm that uses purely local information to converge to the global optimum.
Unlike previous work on distributed mirror descent, we incorporate an integral
feedback in the update, allowing the algorithm to converge with a constant
step-size when discretized. We establish the asymptotic convergence of the
algorithm using Lyapunov stability analysis. We further illustrate numerical
experiments that verify the advantage of adopting integral feedback for
improving the convergence rate of distributed mirror descent.
