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  2. Arbitrary-precision arithmetic - Wikipedia

    en.wikipedia.org/wiki/Arbitrary-precision_arithmetic

    Arbitrary precision is used in applications where the speed of arithmetic is not a limiting factor, or where precise results with very large numbers are required. It should not be confused with the symbolic computation provided by many computer algebra systems , which represent numbers by expressions such as π ·sin(2) , and can thus represent ...

  3. Law of large numbers - Wikipedia

    en.wikipedia.org/wiki/Law_of_large_numbers

    They are called the strong law of large numbers and the weak law of large numbers. [ 16 ] [ 1 ] Stated for the case where X 1 , X 2 , ... is an infinite sequence of independent and identically distributed (i.i.d.) Lebesgue integrable random variables with expected value E( X 1 ) = E( X 2 ) = ... = μ , both versions of the law state that the ...

  4. List of arbitrary-precision arithmetic software - Wikipedia

    en.wikipedia.org/wiki/List_of_arbitrary...

    Go: the standard library package math/big implements arbitrary-precision integers (Int type), rational numbers (Rat type), and floating-point numbers (Float type) Guile: the built-in exact numbers are of arbitrary precision. Example: (expt 10 100) produces the expected (large) result. Exact numbers also include rationals, so (/ 3 4) produces 3/4.

  5. Large numbers - Wikipedia

    en.wikipedia.org/wiki/Large_numbers

    A standardized way of writing very large numbers allows them to be easily sorted in increasing order, and one can get a good idea of how much larger a number is than another one. To compare numbers in scientific notation, say 5×10 4 and 2×10 5 , compare the exponents first, in this case 5 > 4, so 2×10 5 > 5×10 4 .

  6. Law of truly large numbers - Wikipedia

    en.wikipedia.org/wiki/Law_of_truly_large_numbers

    For an event X that occurs with very low probability of 0.0000001%, or once in one billion trials, in any single sample (see also almost never), considering 1,000,000,000 as a "truly large" number of independent samples gives the probability of occurrence of X equal to 1 − 0.999999999 1000000000 ≈ 0.63 = 63% and a number of independent ...

  7. Modular exponentiation - Wikipedia

    en.wikipedia.org/wiki/Modular_exponentiation

    Modular exponentiation is efficient to compute, even for very large integers. On the other hand, computing the modular discrete logarithm – that is, finding the exponent e when given b, c, and m – is believed to be difficult. This one-way function behavior makes modular exponentiation a candidate for use in cryptographic algorithms.

  8. Integer factorization - Wikipedia

    en.wikipedia.org/wiki/Integer_factorization

    To factorize a small integer n using mental or pen-and-paper arithmetic, the simplest method is trial division: checking if the number is divisible by prime numbers 2, 3, 5, and so on, up to the square root of n. For larger numbers, especially when using a computer, various more sophisticated factorization algorithms are more efficient.

  9. List of algorithms - Wikipedia

    en.wikipedia.org/wiki/List_of_algorithms

    Used in Python 2.3 and up, and Java SE 7. ... Fürer's algorithm: an integer multiplication algorithm for very large numbers possessing a very low asymptotic complexity;