is a useful tool in analyzing the efficiency of algorithms. It helps in understanding the and of an algorithm. Time complexity refers to the amount of time an algorithm takes to run, while space complexity determines the amount of memory required by an algorithm.
represents the maximum time an algorithm takes to complete for any given input size. It helps in understanding the upper bound of an algorithm's performance. On the other hand, represents the minimum time an algorithm takes to complete for any given input size. It gives an idea of the lower bound of an algorithm's performance.
considers the average time required by an algorithm to complete for all possible inputs. It provides a more realistic estimation of an algorithm's performance than just considering the best or scenarios.
In terms of Big O notation, represents constant time complexity, meaning the algorithm takes a constant amount of time to complete regardless of the input size. This is considered the scenario. represents linear time complexity, where the running time increases linearly with the input size. denotes logarithmic time complexity, where the running time increases but at a slower rate compared to the input size. indicates quadratic time complexity, where the running time increases exponentially with the input size.
Keywords
o(log n) | best-case time complexity | o(n) | worst-case time complexity | worst case | time complexity | space complexity | big o notation | average-case time complexity | o(n^2) | best case | o(1) |
