A power reformulation continuous-time algorithm for nonconvex distributed constrained optimization over multi-agent systems

Nonconvex distributed constrained optimization over multi-agent systems plays an increasingly important role in control community. However, the duality gap for a nonconvex distributed constrained optimization problem is not always zero. This brings some difficulties for the algorithm design and convergence analysis about such problems. In this paper, to eliminate the duality gap, the original distributed optimization problem is converted to an equivalent distributed one by using a partial power reformulation. Next, for solving this equivalence problem, a continuous-time algorithm is proposed over the multi-agent system where each agent exchanges local information with other agents under an undirected communication graph. It is proved that the proposed continuous-time algorithm locally converges to a strict local optimal solution of the considered original nonconvex distributed optimization problem. Numerical examples show the validness of the presented algorithm. Finally, the algorithm is applied for optimal placement.