A nonlinear optimization algorithm for design, modeling, and diagnosis of networks

This paper presents a fast and highly efficient algorithm for nonlinear optimization and its applications to circuits employing the properties of the norm. The algorithm, based on the work of Hald and Madsen, is similar to a minimax algorithm originated by the same authors. It is a combination of a first-order method that approximates the solution by successive linear programming and a quasi-Newton method using approximate second-order information to solve a system of nonlinear equations resulting from the first-order necessary conditions for an optimum. The new algorithm is particularly useful in fault location methods using the norm. A new technique for isolating the most likely faulty elements, based on an exact penalty function, is presented. Another important application of the algorithm is the design of contiguous-band multiplexers consisting of multicavity filters distributed along a waveguide manifold which is illustrated by a 12-channel multiplexer design. We also present a formulation using the norm for model parameter identification problems in the presence of large isolated errors in measurements and illustrate it with a sixth-order filter.