Interior point SQP strategies for large-scale, structured process optimization problems
نویسندگان
چکیده
Successive Quadratic Programming (SQP) has been the method of choice for the solution of many nonlinear programming problems in process engineering. However, for the solution of large problems with SQP based codes, the combinatorial complexity associated with active set quadratic programming (QP) methods can be a bottleneck in exploiting the problem structure. In this paper, we examine the merits of incorporating an interior point QP method within an SQP framework. This also allows us to provide a novel intepretation of popularly used predictorcorrector interior point (IP) methods using the principles of reduced Hessian SQP (rSQP). Moreover, the resulting large-scale SQP algorithm with an interior point QP, allows us to demonstrate signi cant computational savings on problems drawn from optimal control and nonlinear model predictive control.
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