Summary
We compare the Tangent-on-Tangent and the Tangent-on-Reverse strategies to build programs that compute second derivatives (a Hessian matrix) using automatic differentiation. In the specific case of a constrained functional, we find that Tangent-on-Reverse outperforms Tangent-on-Tangent only above a relatively high number of input parameters. We describe the algorithms to help the end-user apply the two strategies to a given application source. We discuss the modification needed inside the automatic differentiation tool to improve Tangent-on-Reverse differentiation.
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Martinelli, M., Hascoët, L. (2008). Tangent-on-Tangent vs. Tangent-on-Reverse for Second Differentiation of Constrained Functionals. In: Bischof, C.H., Bücker, H.M., Hovland, P., Naumann, U., Utke, J. (eds) Advances in Automatic Differentiation. Lecture Notes in Computational Science and Engineering, vol 64. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68942-3_14
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DOI: https://doi.org/10.1007/978-3-540-68942-3_14
Publisher Name: Springer, Berlin, Heidelberg
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