0
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.
Wiki User
∙ 14y ago
Wiki User
∙ 11y agothe complexity of searching in bst is log(n)
Add your answer:
Earn +20 pts
What is the worst case and best case for interpolation search?
binary search
What is the time complexity for searching an element in an array?
If the array is unsorted, the complexity is O(n) for the worst case. Otherwise O(log n) using binary search.
What is average case complexity of 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/
What is Efficiency of Binary Search tree operations?
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 ) ).
What is the worst case and best case time complexity of heapsort?
The best and worst case time complexity for heapsort is O(n log n).
What is the worst case and best case for interpolation search?
binary search
What is the time complexity for searching an element in an array?
If the array is unsorted, the complexity is O(n) for the worst case. Otherwise O(log n) using binary search.
What is average case complexity of 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/
What is the height of binary search tree in worst case?
In the worst case a binary search tree is linear and has a height equal to the number of nodes. so h=O(h).
Complexity of linear search?
the compexity of linear search in worst case is f(n) = n+1
What is the worst case and best case for binary search?
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).
What is Efficiency of Binary Search tree operations?
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 ) ).
What is the worst case and best case time complexity of heapsort?
The best and worst case time complexity for heapsort is O(n log n).
Advantages of binary search over sequencial search?
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.
What is best and average case of binary search?
Merge sort is O(n log n) for both best case and average case scenarios.
What is the difference between best worst and average case complexity of an algorithm?
These are terms given to the various scenarios which can be encountered by an algorithm. The best case scenario for an algorithm is the arrangement of data for which this algorithm performs best. Take a binary search for example. The best case scenario for this search is that the target value is at the very center of the data you're searching. So the best case time complexity for this would be O(1). The worst case scenario, on the other hand, describes the absolute worst set of input for a given algorithm. Let's look at a quicksort, which can perform terribly if you always choose the smallest or largest element of a sublist for the pivot value. This will cause quicksort to degenerate to O(n2). Discounting the best and worst cases, we usually want to look at the average performance of an algorithm. These are the cases for which the algorithm performs "normally."
What is worst case and average case complexity of linear search algorithm with explanation?
For a list with n elements, the expected cost is the same as the worst-case cost, which is O(n). The average cost will be O(n/2). However, if the list is ordered by probability and geometrically distributed, the complexity becomes constant, O(1). Compare with a binary search which has a cost of O(log n).
