Initialization strategies for optimization of dynamic systems

Abstract For dynamic optimization applications, real-time solution reliability is improved if there is an initialized prior solution that is sufficiently close to the intended solution. This paper details several initialization strategies that are useful for obtaining an initial solution. Methods include warm start from a prior solution, linearization, structural decomposition, and an incremental unbounding of decision variables that leads up to solving the originally intended problem. Even when initialization is not required to solve a dynamic optimization problem, a staged initialization approach sometimes leads to an overall faster solution time when compared to a single optimization attempt. Several challenging optimization problems are detailed that include a high-index differential and algebraic equation pendulum model, a standard reactor model used in many benchmark tests, a tethered aerial vehicle, and smart grid energy storage. These applications are representative of a larger class of applications resulting from the simultaneous approach to optimization of dynamic systems.

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