An Efficient optimization approach for computationally expensive timesteppers using tabulation

A methodology is outlined for the efficient solution of dynamic optimization problems when the system evolution is described by computationally expensive timestepper-based models. The computational requirements issue is circumvented by extending the notion of in situ adaptive tabulation to stochastic systems. Conditions are outlined that allow unbiased estimation of the mapping gradient matrix and, subsequently, expressions to compute the ellipsoid of attraction are derived. The proposed approach is applied towards the solution of two representative dynamic optimization problems, (a) a bistable reacting system describing catalytic oxidation of CO and, (b) a homogeneous chemically reacting system describing dimerization of a monomer. In both cases, tabulation resulted in significant reduction in the solution time of the optimization problem.