Steady-state constrained optimisation for input/output large-scale systems

A model reduction-based, constrained optimisation algorithm for large-scale, steady-state systems is presented. The proposed technique belongs to the reduced Hessian class of methods and involves only low-order Jacobian and Hessian matrices. The reduced Jacobians are computed as projections onto the dominant subspace of the system and are calculated adaptively by numerical directional perturbations. The reduced Hessians are computed the same way, based on a 2-step projection scheme, firstly onto the dominant subspace of the system and secondly onto the subspace of the independent variables. The inequality constraints are handled using constraint aggregation functions. A more efficient version of the proposed algorithm is also presented. The behaviour of the proposed scheme is illustrated through two illustrative case studies including both equality and inequality constraints.