Solution algorithms based on collocation methods are highly suitable for discretizing the underlying dynamic model formulation. Thereafter; the corresponding discretized optimization problem can be solved; e.g. by the interior-point optimizer Ipopt. The performance of the optimizer heavily depends on the availability of derivative information for the underlying optimization problem. Typically; the gradient of the objective function; the Jacobian of the DAEs as well as the Hessian matrix of the corresponding Lagrangian formulation need to be determined. If only some or none of these derivatives are provided; usually numerical approximations are used by the optimizer internally.
OpenModelica supports the Optimica language and is capable of automatically generating the discretized optimization problem using collocation methods as well as the whole symbolic machinery available. In addition; all necessary derivative information is determined using the automatic differentiation capabilities of ADOL-C; which has now been integrated into the OpenModelica environment.
Keywords: Modelica; optimization; automatic differentiation; collocation; OpenModelica; ADOL-C
Proceedings of the 10th International Modelica Conference; March 10-12; 2014; Lund; Sweden
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