Interior point SQP strategies for large-scale, structured process optimization problems

Successive quadratic programming (SQP) has been the method of choice for th e solution of many nonlinear programming problems in process engineering. H owever, for the solution of large problems with SQP based codes, the combin atorial complexity associated with active set quadratic programming (QP) me thods can be a bottleneck in exploiting the problem structure. In this pape r, we examine the merits of incorporating an interior point QP method withi n an SQP framework. This provides a novel interpretation of popularly used predictor-corrector interior point (IP) methods. The resulting large-scale SQP algorithm, with an interior point QP, also allows us to demonstrate sig nificant computational savings on problems drawn from optimal control and n onlinear model predictive control. (C) 1999 Elsevier Science Ltd. All right s reserved.