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To increase the predictive power of a model, one needs to estimate itsunknown parameters. Almost all parameter estimation techniques in ordinarydifferential equation models suffer from either a small convergence region orenormous computational cost. The method of multiple shooting, on the otherhand, takes its place in between these two extremes. The computational cost ofthe algorithm is mostly due to the calculation of directional derivatives ofobjective and constraint functions. Here we modify { the} multiple shootingalgorithm to use the adjoint method in calculating these derivatives. In theliterature, this method is known to be a more stable and computationallyefficient way of computing gradients of scalar functions. A predator-preysystem is used to show the performance of the method and supply all necessaryinformation for a successful and efficient implementation.

A Modified Multiple Shooting Algorithm for Parameter Estimation in ODEs Using Adjoint Sensitivity Analysis