An analog network for geometric optimization problem

An analog neural-type network was developed based on the elastic net approach to solve a general class of geometric optimization problem. In this paper, a mathematical formulation was first described, and then, the evolution process of the network state was qualitatively analyzed. Experimental results show that the algorithm presented here scales well with the network size and can efficiently find the near-optimal solution within hundreds of iterations. >