  This paper addresses non-convex constrained optimization problems that are
characterized by a scalar complicating constraint. We propose an iterative
bisection method for the dual problem (DualBi Algorithm) that recovers a
feasible primal solution with a performance that is progressively improving
throughout iterations. Application to multi-agent problems with a scalar
coupling constraint results in a decentralized resolution scheme where a
central unit is in charge of the update of the (scalar) dual variable while
agents compute their local primal variables. In the case of multi-agent MILPs,
simulations showcase the performance of the proposed method compared with
state-of-the-art duality-based approaches.
