  We consider the problem of parameter estimation in dynamic systems described
by ordinary differential equations. A review of the existing literature
emphasizes the need for deterministic global optimization methods due to the
nonconvex nature of these problems. Recent works have focused on expanding the
capabilities of specialized deterministic global optimization algorithms to
handle more complex problems. Despite advancements, current deterministic
methods are limited to problems with a maximum of around five state and five
decision variables, prompting ongoing efforts to enhance their applicability to
practical problems.
  Our study seeks to assess the effectiveness of state-of-the-art
general-purpose global and local solvers in handling realistic-sized problems
efficiently, and evaluating their capabilities to cope with the nonconvex
nature of the underlying estimation problems.
