A reduced-space interior-point quasi-sequential approach to nonlinear optimization of large-scale dynamic systems

We propose a reduced-space interior-point approach to nonlinear optimization problems with general inequality constraints. It is an extension of the quasi-sequential approach to dynamic optimization of large-scale systems. Inequality constraints are formed by adding slack variables to an equality constrained barrier (interior-point) problem which is solved by a range space step and a null space step in every iteration. Mathematical derivations and computation schemes are presented. We take a highly nonlinear parameter estimation problem as an example to demonstrate the effectiveness of this approach. The result is compared with the full space approach in terms of overall CPU time and number of iterations.