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- 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_AnalysisThe 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).
- https://eng.libretexts.org/Bookshelves/Computer_Science/Databases_and_Data_Structures/Open_Data_Structures_-_An_Introduction_(Morin)/01%3A_Introduction/1.05%3A_Correctness_Time_Complexity_and_Space_ComplexityAmortized running times: If we say that the amortized running time of an operation in a data structure is \(f(\mathtt{n})\), then this means that the cost of a typical operation is at most \(f(\mathtt...Amortized running times: If we say that the amortized running time of an operation in a data structure is \(f(\mathtt{n})\), then this means that the cost of a typical operation is at most \(f(\mathtt{n})\). Expected running times: If we say that the expected running time of an operation on a data structure is \(f(\mathtt{n})\), this means that the actual running time is a random variable (see Section 1.3.4) and the expected value of this random variable is at most \(f(\mathtt{n})\).
- 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_AnalysisThe 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).
