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  • 1.1: Activity 1 - Overview of Algorithm Design and Analysis
    https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_and_Computation_Fundamentals/Algorithm_Design_and_Analysis_(Justo)/01%3A_Fundamental_Design_and_Analysis_Techniques/1.01%3A_Activity_1_-_Overview_of_Algorithm_Design_and_Analysis
    The complexity of an algorithm is a function g(n) that gives the upper bound of the number of operation (or running time) performed by an algorithm when the input size is n. Most of the time, the comp...The complexity of an algorithm is a function g(n) that gives the upper bound of the number of operation (or running time) performed by an algorithm when the input size is n. Most of the time, the complexity of g(n) is approximated by its family O(f(n)) where f(n) is one of the following functions: n (linear complexity), log n (logarithmic complexity), na where a ≥ 2 (polynomial complexity), an (exponential complexity).
  • 26.1: Activity 1 - Overview of Algorithm Design and Analysis
    https://eng.libretexts.org/Courses/Butte_College/Intro_to_Programming_with_Programming_Fundamentals_and_Python_for_Everyone/26%3A_Algorithms/26.01%3A_Activity_1_-_Overview_of_Algorithm_Design_and_Analysis
    The complexity of an algorithm is a function g(n) that gives the upper bound of the number of operation (or running time) performed by an algorithm when the input size is n. Most of the time, the comp...The complexity of an algorithm is a function g(n) that gives the upper bound of the number of operation (or running time) performed by an algorithm when the input size is n. Most of the time, the complexity of g(n) is approximated by its family O(f(n)) where f(n) is one of the following functions: n (linear complexity), log n (logarithmic complexity), na where a ≥ 2 (polynomial complexity), an (exponential complexity).

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