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  2. Factorion - Wikipedia

    en.wikipedia.org/wiki/Factorion

    Let be a natural number. For a base >, we define the sum of the factorials of the digits [5] [6] of , :, to be the following: ⁡ = =!. where = ⌊ ⁡ ⌋ + is the number of digits in the number in base , ! is the factorial of and

  3. Factorial number system - Wikipedia

    en.wikipedia.org/wiki/Factorial_number_system

    The factorial number system is a mixed radix numeral system: the i-th digit from the right has base i, which means that the digit must be strictly less than i, and that (taking into account the bases of the less significant digits) its value is to be multiplied by (i − 1)!

  4. Factorial - Wikipedia

    en.wikipedia.org/wiki/Factorial

    Every sequence of digits, in any base, is the sequence of initial digits of some factorial number in that base. [ 60 ] Another result on divisibility of factorials, Wilson's theorem , states that ( n − 1 ) ! + 1 {\displaystyle (n-1)!+1} is divisible by n {\displaystyle n} if and only if n {\displaystyle n} is a prime number . [ 52 ]

  5. Arbitrary-precision arithmetic - Wikipedia

    en.wikipedia.org/wiki/Arbitrary-precision_arithmetic

    But if exact values for large factorials are desired, then special software is required, as in the pseudocode that follows, which implements the classic algorithm to calculate 1, 1×2, 1×2×3, 1×2×3×4, etc. the successive factorial numbers. constants: Limit = 1000 % Sufficient digits.

  6. Legendre's formula - Wikipedia

    en.wikipedia.org/wiki/Legendre's_formula

    Since ! is the product of the integers 1 through n, we obtain at least one factor of p in ! for each multiple of p in {,, …,}, of which there are ⌊ ⌋.Each multiple of contributes an additional factor of p, each multiple of contributes yet another factor of p, etc. Adding up the number of these factors gives the infinite sum for (!

  7. Computational complexity of mathematical operations - Wikipedia

    en.wikipedia.org/wiki/Computational_complexity...

    Graphs of functions commonly used in the analysis of algorithms, showing the number of operations versus input size for each function. The following tables list the computational complexity of various algorithms for common mathematical operations.

  8. Prefix sum - Wikipedia

    en.wikipedia.org/wiki/Prefix_sum

    Prefix sums are trivial to compute in sequential models of computation, by using the formula y i = y i − 1 + x i to compute each output value in sequence order. However, despite their ease of computation, prefix sums are a useful primitive in certain algorithms such as counting sort, [1] [2] and they form the basis of the scan higher-order function in functional programming languages.

  9. Coin problem - Wikipedia

    en.wikipedia.org/wiki/Coin_problem

    For example, if you had two types of coins valued at 6 cents and 14 cents, the GCD would equal 2, and there would be no way to combine any number of such coins to produce a sum which was an odd number; additionally, even numbers 2, 4, 8, 10, 16 and 22 (less than m=24) could not be formed, either.