The time complexity of the Union Find algorithm is typically O(log n) or better, where n is the number of elements in the data structure.
The time complexity of the algorithm is superpolynomial.
The time complexity of an algorithm with a running time of nlogn is O(nlogn).
The time complexity of an algorithm that uses binary search to find an element in a sorted array in logn time is O(log n).
The time complexity of the algorithm is O(log n).
The time complexity of an algorithm with a factorial time complexity of O(n!) is O(n!).
The time complexity of the algorithm is superpolynomial.
The time complexity of an algorithm with a running time of nlogn is O(nlogn).
The time complexity of an algorithm that uses binary search to find an element in a sorted array in logn time is O(log n).
The time complexity of the algorithm is O(log n).
The time complexity of an algorithm with a factorial time complexity of O(n!) is O(n!).
The time complexity of the Strassen algorithm for matrix multiplication is O(n2.81).
The algorithm will have both a constant time complexity and a constant space complexity: O(1)
The time complexity of the backtrack algorithm is typically exponential, O(2n), where n is the size of the problem.
The time complexity of the backtracking algorithm is typically exponential, O(2n), where n is the size of the problem.
The time complexity of the union find operation is typically O(log n) or O((n)), where n is the number of elements in the data structure.
The average case time complexity of an algorithm is the amount of time it takes to run on average, based on the input data. It is a measure of how efficient the algorithm is in terms of time.
The tight bound for the time complexity of an algorithm is the maximum amount of time it will take to run, regardless of the input size. It helps to understand how efficient the algorithm is in terms of time.