Adaptive Exact Penalty Design for Constrained Distributed Optimization

This paper focuses on a distributed convex optimization problem with set constraints, where the local objective functions are convex but not necessarily differentiable. We employ an exact penalty method for the constrained optimization problem to avoid the projection of subgradients to convex sets, which may result in problems about algorithm trajectories caused by maybe nonconvex differential inclusions and quite high computational cost. To effectively find a suitable gain of the penalty function online, we propose an adaptive distributed algorithm with the help of the adaptive control idea in order to achieve an exact solution without any a priori computation or knowledge of the objective functions. By virtue of convex and nonsmooth analysis, we give a rigorous proof for the convergence of the proposed continuous-time algorithm.