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Description:
This thesis gives an insight on gradient descent methods and how they can be applied to solve parameter identification problems pertaining to an ordinary differential equation (ODE) with unknown parameters. Different gradient descent methods such as stochastic gradient descent, projected gradient descent and projected stochastic gradient descent are outlined and their theoretical convergence behaviour is proven. Further, the theoretical foundation is supported by several numerical experiments as the algorithms' practical performances are analysed. ; published