  To increase the predictive power of a model, one needs to estimate its
unknown parameters. Almost all parameter estimation techniques in ordinary
differential equation models suffer from either a small convergence region or
enormous computational cost. The method of multiple shooting, on the other
hand, takes its place in between these two extremes. The computational cost of
the algorithm is mostly due to the calculation of directional derivatives of
objective and constraint functions. Here we modify { the} multiple shooting
algorithm to use the adjoint method in calculating these derivatives. In the
literature, this method is known to be a more stable and computationally
efficient way of computing gradients of scalar functions. A predator-prey
system is used to show the performance of the method and supply all necessary
information for a successful and efficient implementation.
