$$ p = \sum_{k = 0}^n a_k x^k $$
Horner's method for evaluating a polynomial:
$$ \begin{align} p_H &= a_0 + x \left( a_1 + x \left( a_2 + x \left( a_3 + .... + x \left( a_{n -1} + x a_n \right) \right) \right) \right) \\ &... \\ & = a_0 + x \left( a_1 + x b_2 \right) \\ & = a_0 + x b_1 \\ & = b_0 \end{align}$$
So, if $y = (x - 2)^9$ evaluated with $\Delta x = 10^{-4}$, I get this:
My questions are:
- Why is less precise than directly computing the value of the polynomial?
- Why does the error oscillate like that?
I've tried finding the error in each case:
$$ \Delta p = \Delta x \sum_{k = 1}^n k |a_k| |x^{k - 1}| $$
$$ \begin{align} \Delta p_H &= \Delta x |b_1| + |x| \Delta b_1 \\ &= \Delta x |b_1| + \Delta x |b_2| + |x| \Delta b_2 \\ &... \\ &= \Delta x(|b_1| + |b_2| + ... + |b_n|) + |a_n| |x| \Delta x \\ &= \Delta x \left( \sum_{k = 1}^n |b_n| + |a_n| |x| \right) \end{align}$$
So it seems that
$$ \sum_{k = 1}^n |b_n| + |a_n| |x| > \sum_{k = 1}^n k |a_k| |x^{k - 1}| $$
but by how much? What is the intuition behind this?
