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# Approximate prime counting

*February 17, 2009*

I’m continuing my quest for the special functions coverage in mpmath to match the coverage in Mathematica. Comparisons are not quite fair, because mpmath is (with a few exceptions) numerical-only, and it is missing some heavy-duty functions such as the Meijer G-function (and will for the foreseeable future). Many functions in mpmath are also implemented on smaller domains and less rigorously. On the other hand, Mathematica is more restricted in that it only supports machine-precision exponents. And most importantly, the Mathematica source code is very difficult to read for most people.

In any case, I’m trying to catch up with Mathematica 7, which added a whole slew of functions (not that I’ve caught up with Mathematica 6 or even 5). I did add a few functions in version 0.11 of mpmath as a consequence of seeing them in Mathematica 7 (as I recall right now, the Barnes G-function, hyperfactorial and generalized Stieltjes constants). See also the “Fun with zeta functions” post, which discussed the addition of the Riemann-Siegel functions in mpmath.

I just committed an implementation of the Riemann R function R(*x*) (also discovered in the “new in Mathematica 7″ list), which is an analytic function that closely approximates the prime counting function π(*x*). The incredible accuracy of the Riemann R function approximation can be visualized by plotting it against the exact prime counting funtion (I added a naive implementation of π(*x*) as `primepi`, mainly to facilitate this kind of comparison — SymPy contains a slightly more optimized `primepi` which could be used instead):

>>> from mpmath import *

>>> plot([primepi, riemannr], [0,100])

The accuracy for small *x* is not a fluke, either:

>>> plot([primepi, riemannr], [100000,105000])

The “classical” approximation based on the logarithmic integral is not nearly as good:

>>> plot([primepi, lambda x: li(x) - li(2)], [100000,105000])

The largest exact value of the prime counting function ever computed was π(10^{23}). The Riemann R function is quite easy to evaluate for *x* far larger than that, and the relative accuracy only improves:

>>> mp.dps = 50

>>> print riemannr(10**1000)

4.3448325764011974554109300516481778421781159070188e+996

It is quite likely that no one will ever compute the exact integer that is π(10^{1000}), but the value shown above is correct to every indicated digit. In fact, R(10^{1000}) can be shown using simple estimates to be accurate to about 500 digits.

For the fun of it, I implemented a version of the prime counting function that quickly returns a strict bounding interval for π(*x*), using an inequality by Lowell Schoenfeld:

This gives for example:

>>> mp.dps = 15

>>> print primepi2(10**9)

[50823160.0, 50875310.0]

Comparing the Riemann R function and the exact prime counting function with the lower and upper bounds of the Schoenfeld inequality:

>>> pi1 = lambda x: primepi2(x).a

>>> pi2 = lambda x: primepi2(x).b

>>> plot([primepi, riemannr, pi1, pi2], [10000,12000])

Returning to the example *x* = 10^{1000}, the bounds obtained are:

>>> mp.dps = 1000

>>> v = primepi2(10**1000)

>>> print v.a

43448325764011974554109300516481778421781159070187535206418774067892028956297421

41091652903064607718165071467289250162191865437538175693583349276176983162304817

20028842283993044722261194636177370816661364951186507714125556442852887672847627

37639487556791903286839317671011195810576335127780015765296354832441209488958529

40270929343735968992442702495339548061518980068329967842108151105502256086735702

70840137536441416124939228421984939684274707774026258195286398483405141793241664

48771389205215847692233236114877473504187398786485377346384217825087356612222721

64076496151428324862762772509621406939237565543669854423809507199075669125082789

94753472799321385531846190616064670701763597463167501715697285784529582495283729

84726395093948427825392438044975721449167967563521761421630319216447872905300292

34804577579707557749789078595077525265053645288083485194663875002648919670801486

00507106899091429169697567568603093512170870024325220369457870653525185029701437

3844901850360974589491752273505884338.0

>>> print v.b

43448325764011974554109300516481778421781159070187535206418774067892028956297421

41091652903064607718165071467289250162191865437538175693583349276176983162304817

20028842283993044722261194636177370816661364951186507714125556442852887672847627

37639487556791903286839317671011195810576335127780015765296354832441209488958529

40270929343735968992442702495339548061518980068329967842108151105502256086735702

70840137536441416124939228421984939684274707774026258195286398483405141793241664

48771389205216030926132955971812691701044333600154515219652233647424144532724506

59125521224590219319931601764655406966919866611954863306151010025269888571358859

44632455086296390810620688247562865809602361895967001311288611164573841496810109

47459439386009668941560164405255365722936716670915493742153084879238459437035932

04613524632469949425914711588697691782286908198073032336714125348156636382686152

39874926303969403597018131954944580668821382841922209788091197293915488520205360

8277109394003350202108994842650400209.0

>>> nprint((v.delta)/v.a)

4.21728e-495

>>> nprint((riemannr(10**1000)-v.a)/v.a)

2.10863e-495

and so it turns out that “accurate to about 500 digits” can be replaced by “accurate to at least 494 digits”.

Actually, this is not quite true. Schoenfeld’s inequality depends on the truth of the Riemann hypothesis. Thus, it is conceivable that `primepi2` might return an erroneous interval. If you find an output from `primepi2` that you can prove to be wrong, this must mean either that 1) you’ve found a bug in mpmath, or 2) you’ve disproved the Riemann hypothesis. (One of these options is more likely than the other, and reports should therefore be submitted to the mpmath issue tracker rather than *Annals of Mathematics*.)

On a final note, I’ve also added the function `zetazero` for computing the *n*th nontrivial zero of the Riemann zeta function:

>>> mp.dps = 50

>>> print zetazero(1)

(0.5 + 14.134725141734693790457251983562470270784257115699j)

>>> print zetazero(2)

(0.5 + 21.022039638771554992628479593896902777334340524903j)

>>> print zetazero(10)

(0.5 + 49.773832477672302181916784678563724057723178299677j)

>>> nprint(zeta(zetazero(10)))

(2.14411e-50 - 4.15422e-50j)

It just uses a table to determine correct initial values for the rootfinder, so `zetazero` currently only works up to *n* = 100. In fact, it works up to *n* = 100,000 on an internet-enabled computer; `zetazero` will automatically try to download the table of 100,000 zeros from Andrew Odlyzko’s website if called with *n* > 100. Or it can load a custom url/file if specified by the user. This is one reason why I love Python: downloading a data file, parsing it and converting it to an appropriate internal representation in virtually a single line of code, easily done in the middle of some algorithmic code.

More information about the Riemann R function can be found in “The encoding of the prime distribution by the zeta zeros“, part of Matthew R. Watkins‘ amazing website. The moral of that document is that the error between the exact prime counting and the Riemann R function can be represented by a kind of “Fourier series” involving a summation over the zeros of the Riemann zeta function. I have not gotten to looking at that yet.

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