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Computing Paper 2: 2.3.1
Fichas del conjunto (7)
- 1What is meant by the time complexity of an algorithm?How much time required to solve issue. Measured w/ big-o notation.
Shows limit for time taken in relation to no of data elements.
Allows prediction of time taken for algorithm to finish given no. of data. - 2O(1)Constant time complexity.
Takes same amount of time per element, no matter what the amount of data elements. - 3O(log n)Logarithmic time complexity.
Running time grows in proportion of input size.
E.g. 10 items takes x time, 100 items takes 2x time, 10,000 items takes 4x time. - 4O(n)Linear time complexity.
Running time grows in direct proportion to size of input. More data elements, the longer amount of time needed. - 5O(n^2)Polynomial time complexity.
Running time increases in proportion to a quadratic.
E.g. n squared. - 6O(2^n)Exponential time complexity.
Running time doubles with each addition to input data set. - 7O(n!)Factorial time complexity.
Multiply no. of inputs by each other.
E.g. 3 inputs = 1 * 2 * 3 = 6
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- A level (Computing Paper 2) Computing Fichas sobre Computing Paper 2: 2.3.1, creado por Qashrina Collier el 09/05/2021.
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- Qashrina Collier
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