Automatic Differentiation via Hyper-dual Numbes

On this page you can download a Matlab class for hyper-dual numbers:

I published a paper on arXiv on hyper-dual numbers where I explain how to use the above code to compute the first and second order derivatives of Matlab functions by means of automatic differentiation. Notice that the derivatives are computed in tuple forward mode. More efficient in terms of the number of function calls would be a mixed forward-backward call, i.e.: the gradient is evaluated by adjoint differentiation, and the Hessian is computed from the gradient in tangent-linear mode. An adjoint number class may follow in the future and will then be available for download on this page.

For students: I am happy for contributions or expresses of interest or students who search a thesis topic related to numerical optimization, optimal control and automatic differentiation. I can suggest lots of promising topics in these directions that could substantially support my research.

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