O(n log n) is the lower bound because each of n elements must be compared at least log n times to ensure all permutations are considered

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O(n log n) is the lower bound for comparison-based sorting

O(n log n) is the lower bound because each of n elements must be compared at least log n times to ensure all permutations are considered

Related concepts

merge sort: O(n log n) always

Merge sort consistently performs at O(n log n) time complexity for any input size

Binary search

Time complexity of binary search: O(log n) — halves search space each step

Best, worst and average case

Quicksort's average time complexity is O(n log n)

B-trees optimize: disk-based sorted data with O(log n) reads per query

B-trees optimize disk-based sorted data with O(log n) reads per query

Dijkstra's algorithm

Dijkstra's algorithm time complexity: O((V+E) log V)

Hash table

Hash table lookup: O(1) average time complexity

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