Sort all the function below in increasing order of asymptotic big-O growth. 5n 5n 1³1 3n³ log₂log₂n log₂2 55" log₂ n
Sort all the function below in increasing order of asymptotic big-O growth.
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Big-O notation expresses the upper bound of the growth rate of an algorithm's resource usage. It allows us to classify algorithms into categories based on their efficiency. For example:
(constant time) means the algorithm's performance doesn't depend on the input size. It's very efficient.
(logarithmic time) means the algorithm's performance grows slowly as the input size increases. It's also efficient and often seen in divide-and-conquer algorithms.
(linear time) implies that the algorithm's performance is directly proportional to the input size. It's less efficient than logarithmic time
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