Neural network for non‐linear programming with linear equality constraints
S. Osowski · International Journal of Circuit Theory and Applications · 1992
Abstract This paper presents a simplified approach to neural optimization in the presence of linear equality constraints. In contrast to the standard Lagrangian approach, the constraints simplify the final neural circuit instead of complicating it. the number of elements used is also significantly reduced. Instead of n + t integrators we need only n – t. There is also a similar saving in the number of preprocessing non‐linear devices. Elimination of the constraints allows a large speed‐up of the solution.