Neural Network Method for Solving Linear Fractional Programming

This paper presents a neural network method for solving a class of linear fractional optimization problems with linear equality constraints. The proposed neural network model have the following two properties. First, it is demonstrated that the set of optima to the problems coincides with the set of equilibria of the neural network models which means the proposed model is complete. Second, it is also shown that the model globally converges to an exact optimal solution for any starting point from the feasible region.