In this paper, a generalized neural network with a novel auxiliary function is proposed to solve a distributed non-differentiable optimization over a multi-agent network. The constructed auxiliary function can ensure that the state solution of proposed neural network is bounded, and enters the inequality constraint set in finite time. Furthermore, the proposed neural network is demonstrated to reach consensus and ultimately converges to the optimal solution under several mild assumptions. Compared with the existing methods, the neural network proposed in this paper has a simple structure with a low amount of state variables, and does not depend on projection operator method for constrained distributed optimization. Finally, two numerical simulations and an application in power system are delineated to show the characteristics and practicability of the presented neural network.
