Skip to main content

evaluation - Elegant high precision `log1p`?


Sometimes it is hard to understand how numerical expressions are evaluated. I remember reading claims by Wolfram on how smart the Kernel is to evaluate expressions trees numerically by recognizing patterns, yet I don't see how it applies in very simple examples.


This question is about numerics, but to see if symbolics can help in an elegant way.


It is known that when working with finite precision, the function $\log(1+x)$ should have a special implementation for small $x$. That is why functions like log1p exists in many libraries (on top of log). For example:


/*C code*/ log(1. + 1.e-15) == 1.11022e-15


/*C code*/ log1p(1.e-15) == 1.e-15


(The second version is more exact, the first is "wrong")



In Mathematica:


Log[1. + 1.*^-15] == 1.11022*10^-15


(wrong answer)


Mathematica doesn't have such Log1P function. One can say, well, that is because it doesn't need to, because of the symbolic power. In fact one can know the answer.


N[Log[1 + 1/10^15], 100] = 9.999999999999995000000000000003333333333333330833333333333335333333333333331666666666666668095238095*10^-16


But this is not general, if I want to evaluate Log[1 + x] and x has machine precision then I can't force to use something like log1p. Because 1+x will evaluate to a machine precision number.


These are my attempts:


x = 1.*^-15;
Log[1 + x]
Log[1. + x]

N[Log[1. + N[x, 20]], 20]
N[Log[1 + N[x, 20]], 20]

All evaluate to the wrong answer (1.11022*10^-15)


Finally I find this expression,


N[Log[1 + Rationalize[x, 0]], 20]
9.999999999999995000*10^-16

But really? Is it that hard to get $\log(1+x)$ for small $x$ numerically? Do I have to roll my own Log1p?



Answer




Update:


yode points out in a comment below that there are log1p() and expm1() functions in Mathematica! However they are hidden. They are simply:


Internal`Log1p
Internal`Expm1

They operate only on numeric inputs.


(I don't know at what version these showed up.)


Compare this plot to the one further below using the Log function. The errors really are all zero!


Plot[Internal`Log1p[x]/x - 
N[N[Log[1 + SetPrecision[x, Infinity]], 34]]/x, {x, 0.01, 1},

PlotPoints -> 100, PlotRange -> All, ImageSize -> Large]

errors all zero!




I don't think Mathematica has that function. Seems like it should. (Same for expm1().) You should not need to resort to non-machine arithmetic to get the right answer.


Here is something that will do the trick using only machine arithmetic, if the input is a machine number:


log1p[x_] :=
If[MachineNumberQ[x],
If[x < 0.5,
If[# - 1 == 0, x,

x Divide[Log[#], # - 1]] &[1 + x],
Log[1 + x]],
Log[1 + x]]

If it's not a machine number, then it just gives you Log[1+x].


You can compile the machine number portion for much faster execution (by two orders of magnitude!):


log1px = Compile[{{x, _Real}},
If[x < 0.5,
If[# - 1 == 0, x,
x Divide[Log[#], # - 1]] &[1 + x],

Log[1 + x]], CompilationTarget -> "C"]

The cutoff for x >= 0.5 is where Log[1+x] works just fine, with only the least significant bit varying:


Plot[Log[1 + x]/x - 
N[N[Log[1 + SetPrecision[x, Infinity]], 34]]/x, {x, 0.01, 1},
PlotPoints -> 100, PlotRange -> All, ImageSize -> Large]

things go south below 0.5


Comments

Popular posts from this blog

plotting - Plot 4D data with color as 4th dimension

I have a list of 4D data (x position, y position, amplitude, wavelength). I want to plot x, y, and amplitude on a 3D plot and have the color of the points correspond to the wavelength. I have seen many examples using functions to define color but my wavelength cannot be expressed by an analytic function. Is there a simple way to do this? Answer Here a another possible way to visualize 4D data: data = Flatten[Table[{x, y, x^2 + y^2, Sin[x - y]}, {x, -Pi, Pi,Pi/10}, {y,-Pi,Pi, Pi/10}], 1]; You can use the function Point along with VertexColors . Now the points are places using the first three elements and the color is determined by the fourth. In this case I used Hue, but you can use whatever you prefer. Graphics3D[ Point[data[[All, 1 ;; 3]], VertexColors -> Hue /@ data[[All, 4]]], Axes -> True, BoxRatios -> {1, 1, 1/GoldenRatio}]

plotting - Filling between two spheres in SphericalPlot3D

Manipulate[ SphericalPlot3D[{1, 2 - n}, {θ, 0, Pi}, {ϕ, 0, 1.5 Pi}, Mesh -> None, PlotPoints -> 15, PlotRange -> {-2.2, 2.2}], {n, 0, 1}] I cant' seem to be able to make a filling between two spheres. I've already tried the obvious Filling -> {1 -> {2}} but Mathematica doesn't seem to like that option. Is there any easy way around this or ... Answer There is no built-in filling in SphericalPlot3D . One option is to use ParametricPlot3D to draw the surfaces between the two shells: Manipulate[ Show[SphericalPlot3D[{1, 2 - n}, {θ, 0, Pi}, {ϕ, 0, 1.5 Pi}, PlotPoints -> 15, PlotRange -> {-2.2, 2.2}], ParametricPlot3D[{ r {Sin[t] Cos[1.5 Pi], Sin[t] Sin[1.5 Pi], Cos[t]}, r {Sin[t] Cos[0 Pi], Sin[t] Sin[0 Pi], Cos[t]}}, {r, 1, 2 - n}, {t, 0, Pi}, PlotStyle -> Yellow, Mesh -> {2, 15}]], {n, 0, 1}]

plotting - Mathematica: 3D plot based on combined 2D graphs

I have several sigmoidal fits to 3 different datasets, with mean fit predictions plus the 95% confidence limits (not symmetrical around the mean) and the actual data. I would now like to show these different 2D plots projected in 3D as in but then using proper perspective. In the link here they give some solutions to combine the plots using isometric perspective, but I would like to use proper 3 point perspective. Any thoughts? Also any way to show the mean points per time point for each series plus or minus the standard error on the mean would be cool too, either using points+vertical bars, or using spheres plus tubes. Below are some test data and the fit function I am using. Note that I am working on a logit(proportion) scale and that the final vertical scale is Log10(percentage). (* some test data *) data = Table[Null, {i, 4}]; data[[1]] = {{1, -5.8}, {2, -5.4}, {3, -0.8}, {4, -0.2}, {5, 4.6}, {1, -6.4}, {2, -5.6}, {3, -0.7}, {4, 0.04}, {5, 1.0}, {1, -6.8}, {2, -4.7}, {3, -1....