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Approximation theory is concerned with how functions can best be approximated with simpler functions, and with quantitatively characterizing the errors introduced thereby.
2
votes
Accepted
Approximating exp(x) with a piecewise-linear function accurately
For $b$ not much larger than $a$, the maximum difference between $e^x$ and the line from $(a,e^a)$ to $(b,e^b)$, for $x\in[a,b]$, is approximately $\frac 18 (b-a)^2 e^a$.
So you should choose your poi …
8
votes
Accepted
How to numerically compute $x \ln x$ and related functions near $0$?
Modern mathematics libraries should be able to find $\log x$ precisely for all floating-point numbers, as the algorithms for doing that have long been known and adopted. My experiments on a fairly rec …
1
vote
best approximation to the LambertW(x) or exp(LambertW(x))
As Pietro says, $u=\exp(W(x))$ satisfies $u=x/\ln u$. This is a contraction mapping for large enough $u$, so just start with any old approximation, like $u=x/\ln x$, and do $u:=x/\ln u$ until it conv …
4
votes
Approximation to the ratio of a Gaussian CDF to PDF
If $Y(x)=(1-\Phi(x))/\phi(x)$, it is easy to check that $Y'(x)=xY(x)-1$ and from this anything you like follows by standard methods.
3
votes
Accepted
Approximate Moment Conditions
Here is one approach you can use. The differences $a_k$ provide you with bounds on the difference between the characteristic functions of the two distributions. This may be easy or not depending on …
5
votes
Accepted
Maximum of the weighted binomial sum $2^{-r}\sum_{i=0}^r\binom{m}{i}$
I'll give a crude calculation on the back of this envelope.
Assume that $c'm<r<cm$ for some $0<c'<c<\frac12$.
When $i=o(r^{1/2})$, we have
$$\binom{m}{r-i}= (1+o(1))\, \left(\frac{r}{m-r}\right)^i\bin …
4
votes
Degree necessary of a polynomial?
Version 2
Here is some experimental evidence. Like most people here I'm using $-f(x)$ and assuming $0<b<a<1$.
Consider $b=1/100$ and $a=i/100$.
For $2\le i\le 3$, there is a quadratic polynomial.
F …
3
votes
The relative error of approximating a binomial
My paper here (Adv. Appl. Prob., 21 (1989) 475-478), Theorem 2, provides an estimate over all values of the parameters with relative error that is $o(1)$ if either $\sigma\to\infty$ or $x\sigma\to\inf …