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  2. Time complexity - Wikipedia

    en.wikipedia.org/wiki/Time_complexity

    Graphs of functions commonly used in the analysis of algorithms, showing the number of operations N as the result of input size n for each function. In theoretical computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm.

  3. Computational complexity of mathematical operations - Wikipedia

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

    Here, complexity refers to the time complexity of performing computations on a multitape Turing machine. [1] See big O notation for an explanation of the notation used. Note: Due to the variety of multiplication algorithms, () below stands in for the complexity of the chosen multiplication algorithm.

  4. Stirling's approximation - Wikipedia

    en.wikipedia.org/wiki/Stirling's_approximation

    An alternative version uses the fact that the Poisson distribution converges to a normal distribution by the Central Limit Theorem. [5]Since the Poisson distribution with parameter converges to a normal distribution with mean and variance , their density functions will be approximately the same:

  5. Factorial - Wikipedia

    en.wikipedia.org/wiki/Factorial

    TI SR-50A, a 1975 calculator with a factorial key (third row, center right) The factorial function is a common feature in scientific calculators . [ 73 ] It is also included in scientific programming libraries such as the Python mathematical functions module [ 74 ] and the Boost C++ library . [ 75 ]

  6. Computational complexity theory - Wikipedia

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

    Since the time taken on different inputs of the same size can be different, the worst-case time complexity () is defined to be the maximum time taken over all inputs of size . If T ( n ) {\displaystyle T(n)} is a polynomial in n {\displaystyle n} , then the algorithm is said to be a polynomial time algorithm.

  7. NTIME - Wikipedia

    en.wikipedia.org/wiki/NTIME

    In computational complexity theory, the complexity class NTIME(f(n)) is the set of decision problems that can be solved by a non-deterministic Turing machine which runs in time O(f(n)). Here O is the big O notation, f is some function, and n is the size of the input (for which the problem is to be decided).

  8. Worst-case complexity - Wikipedia

    en.wikipedia.org/wiki/Worst-case_complexity

    In computer science (specifically computational complexity theory), the worst-case complexity measures the resources (e.g. running time, memory) that an algorithm requires given an input of arbitrary size (commonly denoted as n in asymptotic notation). It gives an upper bound on the resources required by the algorithm.

  9. Big O notation - Wikipedia

    en.wikipedia.org/wiki/Big_O_notation

    The sort has a known time complexity of O(n 2), and after the subroutine runs the algorithm must take an additional 55n 3 + 2n + 10 steps before it terminates. Thus the overall time complexity of the algorithm can be expressed as T(n) = 55n 3 + O(n 2). Here the terms 2n + 10 are subsumed within the faster-growing O(n 2). Again, this usage ...