  Inspired and underpinned by the idea of integral feedback, a distributed
constant gain algorithm is proposed for multi-agent networks to solve convex
optimization problems with local linear constraints. Assuming agent
interactions are modeled by an undirected graph, the algorithm is capable of
achieving the optimum solution with an exponential convergence rate.
Furthermore, inherited from the beneficial integral feedback, the proposed
algorithm has attractive requirements on communication bandwidth and good
robustness against disturbance. Both analytical proof and numerical simulations
are provided to validate the effectiveness of the proposed distributed
algorithms in solving constrained optimization problems.
