Binary search is a log n type of search, because the number of operations required to find an element is proportional to the log base 2 of the number of elements. This is because binary search is a successive halving operation, where each step cuts the number of choices in half. This is a log base 2 sequence.
binary search
If the array is unsorted, the complexity is O(n) for the worst case. Otherwise O(log n) using binary search.
Average case complexity for Binary search O(log N). (Big O log n)Habibur Rahman (https://www.facebook.com/mmhabib89)BUBT University Bangladeshhttp://www.bubt.edu.bd/
If it is an unbalanced binary tree, O( ln( n ) / ln( 2 ) ) is best-case. Worst case is O( n ). If it is balanced, worst case is O( ln( n ) / ln( 2 ) ).
The best and worst case time complexity for heapsort is O(n log n).
binary search
If the array is unsorted, the complexity is O(n) for the worst case. Otherwise O(log n) using binary search.
Average case complexity for Binary search O(log N). (Big O log n)Habibur Rahman (https://www.facebook.com/mmhabib89)BUBT University Bangladeshhttp://www.bubt.edu.bd/
In the worst case a binary search tree is linear and has a height equal to the number of nodes. so h=O(h).
The best case for a binary search is finding the target item on the first look into the data structure, so O(1). The worst case for a binary search is searching for an item which is not in the data. In this case, each time the algorithm did not find the target, it would eliminate half the list to search through, so O(log n).
the compexity of linear search in worst case is f(n) = n+1
If it is an unbalanced binary tree, O( ln( n ) / ln( 2 ) ) is best-case. Worst case is O( n ). If it is balanced, worst case is O( ln( n ) / ln( 2 ) ).
The best and worst case time complexity for heapsort is O(n log n).
The worst-case time complexity of quicksort is O(n2), where n is the number of elements in the array being sorted.
Merge sort is O(n log n) for both best case and average case scenarios.
Linear search takes linear time with a worst case of O(n) for n items, and an average of O(n/2). Binary search takes logarithmic time, with a worst and average case of O(n log n). Binary search is therefore faster on average.
The worst case time complexity of heapsort is O(n log n), where n is the number of elements in the input array.