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Suppose that one wants to approximate the 44th Mersenne prime, 2 32,582,657 −1. To get the base-10 logarithm, we would multiply 32,582,657 by log 10 (2), getting 9,808,357.09543 = 9,808,357 + 0.09543. We can then get 10 9,808,357 × 10 0.09543 ≈ 1.25 × 10 9,808,357. Similarly, factorials can be approximated by summing the logarithms of the ...
Because log(x) is the sum of the terms of the form log(1 + 2 −k) corresponding to those k for which the factor 1 + 2 −k was included in the product P, log(x) may be computed by simple addition, using a table of log(1 + 2 −k) for all k. Any base may be used for the logarithm table. [53]
If p is a probability, then p/(1 − p) is the corresponding odds; the logit of the probability is the logarithm of the odds, i.e.: = = = = (). The base of the logarithm function used is of little importance in the present article, as long as it is greater than 1, but the natural logarithm with base e is the one most often used.
[2] A familiar example of logarithmic growth is a number, N , in positional notation , which grows as log b ( N ), where b is the base of the number system used, e.g. 10 for decimal arithmetic. [ 3 ]
In mathematics, for given real numbers a and b, the logarithm log b a is a number x such that b x = a.Analogously, in any group G, powers b k can be defined for all integers k, and the discrete logarithm log b a is an integer k such that b k = a.
Historically', the "common logarithm" was known by its Latin name logarithmus decimalis [2] or logarithmus decadis. [ 3 ] The mathematical notation for using the common logarithm is log( x ) , [ 4 ] log 10 ( x ) , [ 5 ] or sometimes Log( x ) with a capital L ; [ a ] on calculators , it is printed as "log", but mathematicians usually mean ...
Figure 1. Demonstrating log* 4 = 2 for the base-e iterated logarithm. The value of the iterated logarithm can be found by "zig-zagging" on the curve y = log b (x) from the input n, to the interval [0,1].
The precise size that can be guaranteed is not known, but the best bounds known on its size involve binary logarithms. In particular, all graphs have a clique or independent set of size at least 1 / 2 log 2 n (1 − o(1)) and almost all graphs do not have a clique or independent set of size larger than 2 log 2 n (1 + o(1)). [32]