Numerical Differentiation

This module provides functions for computing numerical derivatives of functions.

An adaptive algorithm is used to find the best choice of finite difference and to estimate the error in the derivative.

Usage example

module main

import vsl.diff
import vsl.func
import math

fn pow(x f64, _ []f64) f64 {
	return math.pow(x, 1.5)
}

f := func.new_func(f: pow)
println('f(x) = x^(3/2)')
mut expected := 1.5 * math.sqrt(2.0)
mut result, mut abserr := diff.central(f, 2.0)
println('x = 2.0')
println("f'(x) = ${result} +/- ${abserr}")
println('exact = ${expected}')
expected = 0.0
result, abserr = diff.forward(f, 0.0)
println('x = 0.0')
println("f'(x) = ${result} +/- ${abserr}")
println('exact = ${expected}')