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How to understand the expression of the convergence rate for the number of repetitions
I am learning function approximation and one of the problem that I have trouble in reading is the asymptotic notation.
It is the exercise 6.1.2 of Taylor’s book(p.152)
Suppose there are $L$-densely distributed samples $y_i$ in $[a, b]$. We then choose $x_i^0$ to be one of the $L$ samples and our approximation to $f$ is
$$S(x) = \sum_{i=1}^{n} \frac{(x – x_i^0)}{(x – x_i^0)^2}f(x_i^0) \approx f(x).$$
Theorem 6.1.1 tells that, if $f'(x_i^0)
eq 0$, then
$$S(x) = f(x) + O(\eta(f, x, x^0))$$
in the mean-square sense, where $\eta$ is the linearization error.
What does $O(\eta(f, x, x^0))$ mean here? Does it mean that $S(x)$ will converge linearly to $f(x)$ at least with probability 1 if we repeat the approximation $n$ times to take the average?
Thank you for your help!
A:
The “big-O” notation is only a power rule that we can use to get a bound. It gives a constant we can replace $X$ with to get an