| Academic Open Internet Journal ISSN 1311-4360 |
Volume 17, 2006 |
Unique features of the first perfect number
Dr. David Kisets
University Givat-Ram, Jerusalem, 91904, Israel. E-mail: kisets@netvision.net.il
Abstract: The paper presents the results of investigation demonstrating remarkable properties of the six – the first perfect number, including the complete conformity of all its fractions to such conceptions of perfection as informational optimality, mathematical harmony and balance.
Keywords: Informational optimality; Mathematical harmony; Perfect relations;
Proper fractions.
“Six is a number perfect in itself, and not because God created all things in six days; rather, the converse is true. God created all things in six days because the number is perfect...”
(Saint Augustine (354-430) - The City of God)
Introduction
The paper reveals in the series of so-called perfect numbers (6, 28, 496, 8128, . . .), which meet the Euclid’s requirement: if, for some k > 1, 2k is prime then 2k –1(2k – 1) is a perfect number, the first one possesses features enabling the six be to a far greater extent perfect number than all others.
2) the harmony (harmonious relation): fo = B/A = A/S = 0.618 – the fundamental mathematical constant known as the measure of harmony (named also golden section or golden mean or golden proportion or divine proportion);
3) the balance (equality): λ = A/S = B/S = 0.5 or λo = A/B = 1.
The peculiarity of ρo, as distinct from fo and λ, is in its universality regarding the determination of informatively permissible deviation from any of these constants in real usages of the conceptions concerned. Indeed, in terms of a tolerance optimality and by analogy with measurement error the deviation determined as ± 0.5ro multiplied by the respective constant could reasonably be attributed to any of the constant as the condition of proper usage of respective conception of perfection. Thus, the permissible ranges (PR) connecting to the constants and of their supplements (PRS) to 1 in the normalized presentation (S = 1) are determined as follows:
ρo (1 ± 0.5 ρo) = 0.159 ± 0.013; 1 - ρo (1 ± 0.5 ρo) = 0.841 ± 0.013, (1)
fo (1 ± 0.5 ρo) = 0.618 ± 0.049; 1 - fo (1 ± 0.5 ρo) = 0.382 ± 0.049, (2)
λ (1 ± 0.5 ρo) = 0.5 ± 0.04; 1 - λ (1 ± 0.5 ρo) = 0.5 ± 0.04 (3)
Irrespective of a system whose components represent or have been simulated as dimensional parts, if the relation of the parts match at least one of above expressions, this may be considered as a distinguishing feature of informational perfection.
Perfection of proper fractions for the first perfect number
The location of all possible proper fractions of the first perfect number within permissible ranges determined by means of information constants or their supplements is illustrated in Table 1.
Table 1
|
Informational Constants
|
PR or PRS (permissible ranges) |
1/2 |
1/3 |
1/6 |
2/6 |
3/6 |
2/3 |
|
ρo |
0.146 ¸ 0.172 |
|
|
· |
|
|
|
|
1 - fo |
0.333 ¸ 0.431 |
|
· |
|
· |
|
|
|
λ or (1 – λ) |
0.46 ¸ 0.54 |
· |
|
|
|
· |
|
|
fo |
0.569 ¸ 0.667 |
|
|
|
|
|
· |
Appendix 1: Determining of optimum information coefficients (information cyclicity)
The determination of optimum information coefficients as the fraction 1/2p, i.e. accuracy coefficient and classification coefficient, is carried out applying the conception of weights, which when presented in the normalized form conditionally are treated as formal analogs of probabilities.
Optimum accuracy coefficient
A dimension S may be divided onto n uniform constituents (S/n) with weights Kj = 2(n + 1 – j)/n(n + 1), representing a linear diagram (DK = Kj+1 – Kj = const); and the number j of informative constituents is determined using improved entropy function [3] as follows:
j = exp (-
) = exp {-
(4)
When n ® ¥ the determination of informative parameters with this
formula contains the fundamental estimation error of about 2% due to the
inequality of weights in the redundant part (from j to n) of the linear diagram of weights. This part consists of m
successively reducing subsystems of the complete groups of weights, and thus
possesses own summary redundancy. This local redundancy causes both the
optimization insufficiency and uncertainty in the main informative part (j=1¸j) of the system, and the redundant number of the subsystem parameters is
equal to the product (n - j)
. The same
number of weightiest subsystems parameters may be considered as the limit of
possible addition to j, which is the interval of uncertainty in
determining the informative parameters. The optimum number of parameters (jo) is within the limits from j to [j + (n - j)
], and with estimation error of about 1%
the optimum accuracy coefficient (ρoa) may be determined as follows:
ρoa = 1 - jo/n = 1 –
[j /n + 0.5(1 - j/n)* ![]()
] = 1/2p (5)
Optimum classification coefficient
Being converted into normalized form, non-dividable parts of dimension S represent the weights K1 = A/S and K2 = B/S that, in turn, represent relative contributions to any quality simulated by S. The optimum classification coefficient is evaluated for the necessary and sufficient number jo = exp(-K1 ln K1 – K2 ln K2) = 1.5 of components that is true for the most uncertain (50% confidence) situation about allowing or ignoring the lesser of two components [5]. Accordingly, the classification coefficient with high estimation accuracy is determined as follows:
roc = arg {exp
[- (
)
ln (
)
– (
)
ln (
)]
= 1.5} =
[2] Shannon (1948) A mathematical theory of communication, Bell Syst. Tech, J.27 (1948).
[5] Kisets D. (2003). Information sufficiency of an uncertainty budget. Proceedings of 2nd
InternationalConference on Metrology, November 2003, Eilat, Israel.
Technical College - Bourgas,
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