Interior Point Sqp Strategies For Structured Process Optimization Problems

Successive Quadratic Programming (SQP) has been the method of choice for the solution of nonlinear programming problems. For the solution of large problems with SQP based codes, the combinatorial complexity associated with active set methods for the treatment of inequalities has been a bottleneck in exploiting the problem structure. In this paper, we examine the merits of incorporating an interior point method within an SQP framework and the significant computational savings are demonstrated on problems drawn from optimal control. We also provide a novel intepretation of the popularly used predictor-corrector interior point method using the principles of reduced Hessian SQP (rSQP).