In this paper, we consider a class of k-step linear multistep methods in the form (1.1) of numerical differentiation (N.D.) formulas. For each k, we have required the property of A-stability which ...
We investigate a novel adaptive choice rule of the Tikhonov regularization parameter in numerical differentiation which is a classic ill-posed problem. By assuming a ...
We demonstrate the flexibility and ease of use of C++ algorithmic differentiation (AD) tools based on overloading through application to numerical patterns (kernels) arising in computational finance.
In this paper, we apply stochastic (backward) automatic differentiation to calculate stochastic forward sensitivities. A forward sensitivity is a sensitivity at a future point in time, conditional on ...
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