What question did this study set out to answer?

The aim is to preserve the accuracy of numerical derivatives when using approximate or noisy function values.

Numerical Differentiation of Approximated Functions with Limited Order-of-Accuracy Deterioration

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial demonstrates a novel differentiation algorithm improving accuracy in noisy function data, indicating significant efficiency gains.

The aim is to preserve the accuracy of numerical derivatives when using approximate or noisy function values.
Developed the LDC method for numerical differentiation with limited order-of-accuracy loss.
Applied the method to various functions on possibly irregular grids.
Compared the LDC algorithm with existing differentiation techniques for performance evaluation.
The LDC method achieves a derivative accuracy of order O(h^{r'}) for all r' < r, preserving more accuracy than traditional methods.
Demonstrated a minimal loss in order of accuracy even for non-smooth functions.
Illustrated efficient algorithm performance with numerical results validating theoretical claims.

Bruno et al. (2012) studied this question.

View Full Paper

Ask AI

Bookmark

Randomized trial demonstrates a novel differentiation algorithm improving accuracy in noisy function data, indicating significant efficiency gains.