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Based on algorithmic differentiation, the authors present a derivative code compiler capable of transforming implementations of multivariate vector functions into a program for computing derivatives. Its unique reapplication feature allows the generation of code of an arbitrary order of differentiation, where resulting values are still accurate up to machine precision compared to the common numerical approximation by finite differences. The high memory load resulting from the adjoint model of Algorithmic Differentiation is circumvented using semi-automatic inter-procedural check-pointing enabled by the joint reversal scheme implemented in their compiler.
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