A Convergent Algorithm for L2 Optimal Mimo Model Reduction
Often, the exact model of industrial processes turns out to be too complex for simulation and controller design. It is therefore mandatory to simplify the mathematical description of the process and/or the one of the controller. A particularly attractive simplification criterion is related to the minimization of the L 2 norm of the approximation error. This paper presents an algorithm for solving the L 2-optimal MIMO model reduction problem. It is shown that its convergence to the minima of the approximation error norm is guaranteed. The algorithm proves to be fast and efficient compared to other algorithms suggested in the literature to the same purpose.
In this article, we study nonsmooth convex minimax programming problems with cone constraint and abstract constraint. Our aim is to develop sequential Lagrange multiplier rules for this class of problems in the absence of any constraint qualification. These rules are obtained in terms of ∊-subdifferentials of the functions. As an application of these rules, a sequential dual is proposed and sequential duality results are presented.