Thomas R. Nicely
Last modified....................0700 GMT 06 October 2011. Mathematical Reviews.............MR1853722 (2003d:11184). Journal citation.................Virginia Journal of Science 52:1 (Spring, 2001) 45-55. Printing date....................April 2001. Accepted for publication.........26 January 2001. Accepted for consideration.......09 November 2000. Original submission..............06 November 2000 (Virginia Journal of Science).
The present study results from the continuation of a project initiated in 1993, with results to 1e14 previously published (Nicely, 1995). A detailed description of the general problem, the computational methods employed, and the incidental discovery of the Pentium FDIV flaw may be found there, with additional details given in (Nicely, 1999); only a brief summary will be included here.
The prime numbers themselves continue to retain most of their secrets, but still less is known about the twin primes. A matter as fundamental as the infinitude of K_2 remains undecided---the famous "twin-primes conjecture." Nonetheless, Brun (1919) proved that in any event the sum of the reciprocals,
(1) B_2 = (1/3 + 1/5) + (1/5 + 1/7) + (1/11 + 1/13) + (1/17 + 1/19) + ... ,
is convergent, in contrast to the known divergence of the sum of the reciprocals of all the primes (Brun actually omitted the first term in parentheses, which of course does not affect the convergence). The limit of this sum, styled Brun's sum or Brun's constant, is often denoted as simply B, but henceforth the author will use B_2. In this instance, as in a number of others noted in this paper, identifiers have been changed from those in (Brent, 1975) and (Nicely, 1995), in anticipation of the need for analogous symbols to be used in the study of prime constellations other than the twins.
The twin-primes conjecture is a consequence of a much stronger result, an asymptotic relationship conjectured by Hardy and Littlewood (1922/23, pp. 42-44):
(2) pi_2(x) ~ L_2(x) = 2c_2*integral[1/(ln(t))^2, t, 2, x] ,
where pi_2(x) represents the count of twin-prime pairs (q, q+2) such that q <= x, and c_2 denotes the "twin-primes constant," computed to 42D by Wrench (1961),
(3) c_2 = 0.66016 18158 46869 57392 78121 10014 55577 84326 23...
The validity of the conjecture (2), sometimes titled the Hardy-Littlewood approximation, is central to the estimation of Brun's constant and the error bounds in this paper. The Hardy-Littlewood approximation is itself a consequence of the yet more general "prime k-tuples conjecture," also set forth in their 1922/23 work. See Riesel (1994, pp. 60-83) for an illuminating exposition of these concepts.
Although (1) is convergent, the monotonically increasing partial sums approach the limit with agonizing slowness; summing the first thousand million reciprocals is still insufficient to bring us within five percent of the estimated value of the limit. However, assuming the validity of the Hardy-Littlewood approximation (2), a first-order extrapolation was derived by Fröberg (1961) and further studied by Brent (1975),
(4) B_2 = S_2(x) + 4c_2/ln(x) + O(1/(sqrt(x)*ln(x))) ,
with an accelerated rate of convergence O(sqrt(x)) faster than (1). Here S_2(x) is the partial sum
(5) S_2(x) = sum(1/q + 1/(q+2), q, q <= x) .
of the reciprocals of all the twin-prime pairs (q, q+2) for which q <= x. Note that S_2(x) is written as B(x) in (Brent, 1975) and (Nicely, 1995). The first-order extrapolation of S_2(x) to approximate B_2 consists of the first two terms of the right hand side of (4); this was indicated as B*(x) in (Brent, 1975) and (Nicely, 1995), but we write it here as F_2(x):
(6) F_2(x) = S_2(x) + 4c_2/ln(x) .
The final term in (4) is the author's conjectured error or remainder term, inspired by Brent's (1975) probabilistic analysis. As discussed in Shanks and Wrench (1974, p. 298), no effective second-order extrapolation is known.
As mentioned previously, additional details regarding the computational technique, and the Pentium FDIV affair, are available in (Nicely, 1995, 1999), and also at the author's URL.
(7) delta_2(x) = L_2(x) - pi_2(x) ;
the partial sums S_2(x) of the reciprocals of the twins; and the first-order extrapolations F_2(x) of S_2(x) to the limit, according to (6), members of a sequence believed to be converging to Brun's constant B_2. Note that the discrepancy delta_2(x) was written in (Brent, 1975) and (Nicely, 1995) as r_3(x); Brent also rounded this value to the nearest integer.
TABLE 1. Counts of twin-prime pairs and estimates of Brun's constant. ============================================================================= x pi_2(x) delta_2(x) S_2(x) F_2(x) ============================================================================= 1.0e+01 2 2.84 0.8761904761904761905 2.0230090113326 1.0e+02 8 5.54 1.3309903657190867570 1.9043996332901 1.0e+03 35 10.80 1.5180324635595909885 1.9003053086070 1.0e+04 205 9.21 1.6168935574322006462 1.9035981912177 1.0e+05 1224 24.71 1.6727995848277415480 1.9021632918562 1.0e+06 8169 79.03 1.7107769308042211063 1.9019133533279 1.0e+07 58980 -226.18 1.7383570439172709388 1.9021882632233 1.0e+08 440312 55.79 1.7588156210679749679 1.9021679379607 1.0e+09 3424506 802.16 1.7747359576385368007 1.9021602393210 1.0e+10 27412679 -1262.47 1.7874785027192415475 1.9021603562335 1.0e+11 224376048 -7183.32 1.7979043109551191615 1.9021605414226 1.0e+12 1870585220 -25353.18 1.8065924191758825917 1.9021606304377 1.0e+13 15834664872 -66566.94 1.8139437606846070596 1.9021605710802 1.0e+14 135780321665 -56770.51 1.8202449681302705289 1.9021605777833 2.0e+14 259858400254 -286596.19 1.8219692563019236634 1.9021605806674 3.0e+14 380041003032 -386165.49 1.8229446574498899187 1.9021605813179 4.0e+14 497794845572 -687458.42 1.8236224494488219106 1.9021605828234 5.0e+14 613790177314 -495402.94 1.8241402488570614635 1.9021605819011 6.0e+14 728412916123 -399030.90 1.8245582810368460212 1.9021605816028 7.0e+14 841912734248 -330271.47 1.8249082431039834264 1.9021605813540 8.0e+14 954464283498 -207253.20 1.8252088524969516994 1.9021605810407 9.0e+14 1066196920739 -459168.78 1.8254720744000806297 1.9021605816527 1.0e+15 1177209242304 -750443.32 1.8257060132402797152 1.9021605822498 1.1e+15 1287579137984 -732612.87 1.8259164099409972759 1.9021605822159 1.2e+15 1397370335220 -761338.54 1.8261074785718993129 1.9021605822802 1.3e+15 1506635099560 -762644.45 1.8262824008978027694 1.9021605822837 1.4e+15 1615417411648 -785068.05 1.8264436378766369280 1.9021605823288 1.5e+15 1723754585354 -761213.67 1.8265931311402050729 1.9021605823084 1.6e+15 1831678961614 -851925.37 1.8267324395006005931 1.9021605824283 1.7e+15 1939218595600 -1129122.83 1.8268628327687977085 1.9021605827604 1.8e+15 2046397121805 -678331.73 1.8269853577548725890 1.9021605822393 1.9e+15 2153237307407 -562823.58 1.8271008903959923363 1.9021605821153 2.0e+15 2259758303674 -612652.24 1.8272101680098151140 1.9021605821628 2.1e+15 2365977242191 -653062.89 1.8273138179643056714 1.9021605822014 2.2e+15 2471909670028 -643465.53 1.8274123785364204712 1.9021605821937 2.3e+15 2577569863563 -750111.35 1.8275063150448871463 1.9021605822851 2.4e+15 2682970233099 -552427.29 1.8275960317894826243 1.9021605821145 2.5e+15 2788122612616 -168258.89 1.8276818830618905359 1.9021605818032 2.6e+15 2893038573759 -430246.96 1.8277641812367275962 1.9021605820124 2.7e+15 2997726948096 -292107.29 1.8278432012390461693 1.9021605819106 2.8e+15 3102197972961 -876051.32 1.8279191890118998763 1.9021605823359 2.9e+15 3206458423771 -521046.38 1.8279923621701145073 1.9021605820865 3.0e+15 3310517800844 -897422.15 1.8280629180352850193 1.9021605823404 =============================================================================
(8) B_2 = 1.90216 05823 +/- 0.00000 00008 .
The error estimate is believed to define a 95 % confidence interval for the value of B_2. I have no rigorous proof of this assertion regarding the error estimate; rather it is an inference from the analysis (presented below) of the available numerical data. The notion of a "95 % confidence interval" is to be interpreted as follows. Based on the available numerical data, the author believes that whenever the technique used for this error analysis is applied to a sufficiently numerous sample of distinct integers x > 1, Brun's constant B_2 will lie between F_2(x) - E_2(x) and F_2(x) + E_2(x) for at least 95 % of the integers in the sample. Here E_2(x) is the error bound function stated in (11) below; the error estimate given in (8) is a special case of this error bound function, namely E_2(x_0). More precisely, given any set Z_1 of distinct integers x > 1, there will always exist a superset Z_2 of distinct integers x > 1, Z_1 \subseteq Z_2, such that F_2(x) - E_2(x) <= B_2 <= F_2(x) + E_2(x) for at least 95 % of the integers in Z_2.
The algorithm for obtaining and validating this error bound function will now be explained. Discussion and justification of certain details of the procedure will be deferred until a later point in this paper.
(A) A set S of sample test points is chosen from the available numerical data; this set should be a reasonably large subset of all the available data points, avoiding any known bias in the associated values of S_2 or F_2. Indeed, S might be chosen as the entire set T of all recorded data points, up to and including the current upper bound x_0 = 3e15 of computation; there are 300081 points in T, consisting of the lattice (1e10)(1e10)(x_0) together with the "decade values" x = k*10^n, (k=1...9, n=1...9). However, the calculations to be carried out in the error analysis then become excessive. We choose instead for S the lattice (1e12)(1e12)(x_0), consisting of 3000 equally spaced data points, extending to the current upper limit of computation, the increment being one (U. S.) trillion.
(B) For each x in S, we obtain an error bound on F_2(x), presumably representing a 95 % confidence interval, by determining the value of a parameter K_95(x) such that, for at least 95 % of the points in the set U = {t: t in T, t <= x/2},
(9) |F_2(x) - F_2(t)| < K_95(x) /(sqrt(t)*ln(t)) .
Here the form of the "scaling factor" in the denominator is inferred from the remainder term conjectured in (4). The data points t > x/2 are excluded from U to minimize any artificial reduction in the error estimate resulting from the implicit bias of F_2(t) toward F_2(x) as t approaches x.
(C) We now reason as follows. Since for each x in S, at least 95 % of the (relevant) preceding extrapolations F_2(t), t in U, agree with F_2(x) within the bound in (9), we assume that this property will remain valid for arbitrarily large values of x as well. We now interchange x and t in (9), as well as the order of the resulting terms on the left hand side, and take the limit as t approaches plus infinity.
(10) |F_2(x) - lim(F_2(t), t, +infinity)| <= lim(K_95(t), t, +infinity)/(sqrt(x)*ln(x)) .
The numerical evidence indicates that the positive function K_95(x) is either roughly constant, or exhibits an overall decreasing trend masked by small scale variations (see Table 2). Thus we can obtain an approximate upper bound on the error by using K_95(x) in place of the (unknown) limit of K_95(t) in (10). This produces the desired error bound function E_2(x):
(11) |F_2(x) - B_2| <= E_2(x) = K_95(x)/(sqrt(x)*ln(x)) .
Determination of the error bound at any specific x then becomes a matter of calculating K_95(x) and substituting into (11).
Analysis of the data yields the value K_95(x_0) = 1.380. Substitution into (11) then gives
(12) E_2(x_0) = 1.380/(sqrt(x_0)*ln(x_0)) = 0.00000 00007 06989 .
Rounding up produces the error estimate stated in (8).
The validation process consisted of comparing the confidence intervals obtained for B_2 at each x in the "lower half" S' = {x: x in S, x <= x_0/2} of S (the values near x_0 being excluded for reasons similar to those given for set U) with the (presumably) best value obtained at x_0. Simply put, the issue is this: what percentage of the confidence intervals obtained for each x in S' actually contain the best known point estimate for B_2, given in (8) (and to greater precision, if not accuracy, in the last entry of Table 1)? For example, applying our error analysis technique to the data for x <= 1e14 yields K_95(1e14) = 1.758, and substitution into (11) then produces the confidence interval B_2 = 1.90216 05777 83 +/- 0.00000 00054 53. Since our best estimate for B_2 lies within this interval, we consider the error estimate algorithm to be a success at x = 1e14. On the other hand, applying the algorithm to the data for x <= x_1 = 8.13e14, we obtain K_95(x_1) = 1.306, with the resulting confidence interval B_2 = 1.90216 05809 53 +/- 0.00000 00013 34, which constitutes a failure.
A survey of all the points x in S' reveals that 96.93 % (1454 of 1500) produce confidence intervals containing our current best point estimate for B_2. These calculations, briefly summarized in Table 2, also show the trends in the values of K_95(x), E_2(x), and the cumulative percentage of successful (in the sense described above) error estimates generated by the algorithm. The available data thus indicates that our algorithm has been successful (actually performing beyond expectation) in producing valid 95 % confidence intervals for the estimates of B_2. Therefore we anticipate that the error bounds thus obtained for larger values of x, including our current upper bound of computation x_0 = 3e15, will also yield valid 95 % confidence intervals for the value of B_2.
TABLE 2. Performance data for the error analysis algorithm. =============================================================== x/1e12 K_95(x) E_2(x)*1e10 Success % =============================================================== 1 2.074 750.61 100.00 10 2.218 234.32 80.00 100 1.758 54.53 94.00 200 1.602 34.40 83.50 300 1.582 27.40 89.00 400 2.047 30.44 91.75 500 1.688 22.30 93.40 600 1.564 18.76 94.50 700 1.451 16.04 94.86 800 1.320 13.60 95.25 900 1.487 14.39 94.89 1000 1.658 15.18 95.40 1100 1.623 14.13 95.82 1200 1.626 13.52 96.17 1300 1.608 12.82 96.46 1400 1.606 12.31 96.71 1500 1.584 11.70 96.93 1600 1.612 11.51 97.12 1700 1.747 12.08 97.29 1800 1.511 10.14 97.44 1900 1.455 9.49 97.58 2000 1.454 9.23 97.70 2100 1.450 8.97 97.81 2200 1.434 8.65 97.91 2300 1.449 8.54 98.00 2400 1.381 7.96 98.08 2500 1.269 7.16 98.16 2600 1.321 7.30 98.23 2700 1.277 6.92 98.30 2800 1.399 7.43 98.36 2900 1.307 6.82 98.41 3000 1.380 7.07 98.47 ===============================================================
TABLE 3. Impact of various scaling factors on the error analysis. ============================================================================ Scaling factor K_95 Error Success % ============================================================================ sqrt(x)*ln(x) 1.380 7.07 96.93 sqrt(x)*ln(x)*ln(ln(x)) 4.809 6.89 96.27 sqrt(x)*[ln(x)]^2 45.40 6.53 94.80 sqrt(x)*ln(x)*[(ln(ln(x))]^2 16.80 6.74 95.67 sqrt(x)*ln(x)*ln(ln(x))*ln(ln(ln(x))) 6.016 6.77 95.87 sqrt(x) 0.043 7.83 99.00 ============================================================================
|B*(x) - B| < E_2(x) = 4.14083/(sqrt(x)*ln(x))See the referenced paper for details. By this formula, the result of the calculations up to 3e15 becomes
B = 1.90216 05823 +/- 0.00000 00022Furthermore, although the cited paper represents this error bound as having a 99 % confidence level, the author is of the opinion (2009) that this error bound is best characterized as representing only "at least one standard deviation," i.e., a confidence level of at least 68.27 %.