The goal of this project is to compute the derivative of a function using finite difference formulas. The difficulty with these formulas is that it must use a step which must be neither too large (otherwise the truncation error dominates the error) nor too small (otherwise the condition error dominates). For this purpose, it provides exact methods (based on the value of higher derivatives) and approximate methods (based on function values). Furthermore, the module provides finite difference formulas for the first, second, third or any arbitrary order derivative of a function. Finally, this package provides 15 benchmark problems for numerical differentiation.
This module makes it possible to do this:
import math
import numericalderivative as nd
def scaled_exp(x):
alpha = 1.0e6
return math.exp(-x / alpha)
h0 = 1.0e5 # This is the initial step size
x = 1.0e0
algorithm = nd.SteplemanWinarsky(scaled_exp, x)
h_optimal, iterations = algorithm.compute_step(h0)
f_prime_approx = algorithm.compute_first_derivative(h_optimal)- Michaël Baudin, 2024
To install from Github:
git clone https://github.com/mbaudin47/numerical_derivative.git
cd numerical_derivative
python setup.py installTo install from Pip:
pip install numericalderivative- Gill, P. E., Murray, W., Saunders, M. A., & Wright, M. H. (1983). Computing forward-difference intervals for numerical optimization. SIAM Journal on Scientific and Statistical Computing, 4(2), 310-321.
- Adaptive numerical differentiation R. S. Stepleman and N. D. Winarsky Journal: Math. Comp. 33 (1979), 1257-1264
- Dumontet, J., & Vignes, J. (1977). Détermination du pas optimal dans le calcul des dérivées sur ordinateur. RAIRO. Analyse numérique, 11 (1), 13-25.
- Implement the method of:
Shi, H. J. M., Xie, Y., Xuan, M. Q., & Nocedal, J. (2022). Adaptive finite-difference interval estimation for noisy derivative-free optimization. SIAM Journal on Scientific Computing, 44(4), A2302-A2321.