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Showing 1 to 9 of 9 for “"algorithmic differentiation"”.
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Adjoint Venture: Fast Greeks with Adjoint Algorithmic Differentiation
… ideas are extended to the paradigm of Adjoint Algorithmic Differentiation, and it is illustrated how the use of sophisticated techniques within this space can further improve the ease of use and efficiency of sensitivity calculations.
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Accelerated Adjoint Algorithmic Differentiation with Applications in Finance
Adjoint Differentiation's (AD) ability to calculate Greeks efficiently and to machine precision while scaling in constant time to the number of input variables is attractive for calibration and hedging where frequent calculations are required. Algorithmic adjoint differentiation tools automatically …
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Multi-parameter estimation in glacier models with adjoint and algorithmic differentiation
… and often require discretizing additional PDE's. Algorithmic differentiation (AD) decomposes the model into a composite of elementary operations (+, -, *, /, etc ... ) and a source-to-source transformation generates code for the Jacobian and its transpose for each operations. Derivatives computed …
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Multi-CubeSat mission planning enabled through parallel computing
… and Jacobian are developed in real time using algorithmic differentiation, which allows for significantly higher accuracy over traditional finite difference methods. This framework is tested against analytical methods developed by Wiesel for in-plane, and Edelbaum and Kechichian for out of …
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THE THEORY OF COMBINATORY DIFFERENTIATION AND LOCALITY IN QUANTUM CHEMISTRY
… Chapter one is the theory of combinatory differentiation, which practically brings symbolic differentiation up to speed with algorithmic differentiation and enables the analytic automation of the backpropagation and differential tensor calculus. At the center of this model of …
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Geometric numerical integration for optimisation
… parameter-dependent solution mapping, and study algorithmic differentiation approaches to evaluating the derivatives. Furthermore, we prove that the algorithmic derivatives converge to the implicit derivatives. Thus we demonstrate that, although some parameter tuning problems must inevitably be …
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Uncertainty quantification in ocean state estimation
… model (MITgcm) are generated by means of algorithmic differentiation (AD). Computational complexity of the Hessian code is reduced by tangent linear differentiation of the adjoint code, which preserves the speedup of adjoint checkpointing schemes in the second derivative calculation. A …
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Uncertainty Quantification in ocean state estimation
… model (MITgcm) are generated by means of algorithmic differentiation (AD). Computational complexity of the Hessian code is reduced by tangent linear differentiation of the adjoint code, which preserves the speedup of adjoint checkpointing schemes in the second derivative calculation. A …