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The Error function

$\mathrm{erf}(x)=\frac{2}{\sqrt{\pi}}\int_0^x e^{-t^2}\,dt$

shows up in many contexts, but can't be represented using elementary functions.

I compared it with another function $f$ which also starts linearly, has $f(0)=0$ and converges against the constant value 1 fast, namely

$\tanh{(x)} = \frac {e^x - e^{-x}} {e^x + e^{-x}}$.

Astoningishly to me, I found that they never differ by more than $|\Delta f|=0.0812$ and converge against each other exponentially fast!

I consider $\tanh{(x)}$ to be the somewhat prettyier function, and so I wanted to find an approximation to $\text{erf}$ with "nice functions" by a short expression. I "naturally" tried

$f(x)=A\cdot\tanh(k\cdot x^a-d)$

Changing $A=1$ or $d=0$ on it's own makes the approximation go bad and the exponent $a$ is a bit difficult to deal with. However, I found that for $k=\sqrt{\pi}\log{(2)}$ the situation gets "better". I obtained that $k$ value by the requirement that "norm" given by


i.e. the difference of the functions areas, should valish. With this value, the maximal value difference even falls under $|\Delta f| = 0.03$. And however you choose the integration bounds for an interval, the area difference is no more than $0.017$.

enter image description here

Numerically speaking and relative to a unit scale, the functions $\text{erf}$ and $\tanh{(\sqrt{\pi}\log{(2)}x)}$ are essentially the same.

My question is if I can find, or if there are known, substitutions for this non-elementary function in terms of elementary ones. In the sense above, i.e. the approximation is compact/rememberable while the values are even better, from a numerical point of view.

The purpose being for example, that if I see somewhere that for a computation I have to integrate erf, that I can think to myself "oh, yeah that's maybe complicated, but withing the bounds of $10^{-3}$ usign e.g. $\tanh(k\cdot x)$ is an incredible accurate approximation."

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Related article: A handy approximation for the error function and its inverse. – NikolajK Jun 10 '14 at 19:10

It depends on how much accuracy you need and over what interval. It seems that you are happy with a few percent. There is an approximation in Abromowitz & Stegun that gives $\text{erf}$ in terms of a rational polynomial times a Gaussian over $[0,\infty)$ out to $\sim 10^{-5}$ accuracy.

In case you care, in the next column, there is a series for erf of a complex number that is accurate to $10^{-16}$ relative error! I have used this in my work and got incredible accuracy with just one term in the sum.

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Do you happen to know what the integrals of those approximations are (from negative to positive infinity)? I'm asking for the cases where we need to avoid letting the total area go over 1. – Mehrdad Jan 24 '14 at 5:06
Hello. Following your link to Abromowitz & Stegun, one can read that they borrowed those approximations from Hasting: Approximation for digital computers, but Hastings as well as A&S doen't provide any explanation, how to obtain those approximations. Do you happen to know how to do that or where this has been done? Thank you. – Antoine Jul 10 '15 at 18:05

I suspect the reason the $\tanh x$ solution "works" so well is because it happens to be the second order Pade approximation in $e^x$. unfortunately, higher order Pade Approximations don't seem to work as well. One more thing you could due is try to approximate $\text{erf}(x)$ only on $(-3,3)$, and assume it to be $\pm 1$ everywhere else.

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I pointed out this close correspondence in Section 2.4 of L. Ingber, ``Statistical mechanics of neocortical interactions. I. Basic formulation,'' Physica D 5, 83-107 (1982). [ URL ]

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