The time complexity of searching in an AVL tree is O(log n) due to its balanced nature.

In a balanced binary search tree, the time complexity for searching an element is O(log n).

In the worst case, a binary search tree can become unbalanced, resembling a linked list, leading to a time complexity of O(n) for search operations.

In a balanced binary search tree, the average time complexity for searching an element is O(log n) because each comparison allows the search to skip about half of the tree.

In an ideal hash table with no collisions, searching for an element can be done in constant time O(1).

Searching for an element in a queue implemented using an array requires checking each element, resulting in a time complexity of O(n).

The time complexity of searching for an element in a Red-Black tree is O(log n) due to its balanced nature.

The time complexity of searching for an element in a sorted array using binary search is O(log n).

Searching for an element in a stack takes O(n) time in the worst case, as you may need to traverse all elements.

The time complexity for searching in an AVL tree is O(log n) due to its balanced nature, which ensures that the height of the tree is logarithmic with respect to the number of nodes.

Searching for an element in an unsorted array requires checking each element, resulting in O(n) time complexity.

Searching in an unsorted linked list requires traversing the list, leading to O(n) time complexity.

The average time complexity of QuickSort is O(n log n), making it efficient for sorting large arrays.

The best-case time complexity for Insertion Sort is O(n) when the array is already sorted.

The best-case time complexity of Quick Sort is O(n log n) when the pivot divides the array into two equal halves.

Binary search has a time complexity of O(log n) because it divides the search interval in half with each step.

Bubble sort has a worst-case time complexity of O(n^2) due to the nested loops.

Bubble sort has a worst-case time complexity of O(n^2) due to the nested loops.

DFS also explores all vertices (V) and edges (E), resulting in a time complexity of O(V + E).

The time complexity of the dynamic programming solution for the 0/1 Knapsack problem is O(n * W), where n is the number of items and W is the maximum weight capacity.

The time complexity of the dynamic programming solution for the edit distance problem is O(n*m), where n and m are the lengths of the two strings.

The time complexity of the dynamic programming solution for the Fibonacci sequence is O(n) because it computes each Fibonacci number only once and stores the results.

Using dynamic programming, the Fibonacci sequence can be computed in O(n) time by storing previously computed values.

Inorder traversal visits each node exactly once, resulting in a time complexity of O(n), where n is the number of nodes in the tree.

The time complexity of the K-means algorithm is O(nk), where n is the number of data points and k is the number of clusters.

The longest common subsequence problem can be solved using a dynamic programming approach with a time complexity of O(n*m), where n and m are the lengths of the two sequences.

The longest increasing subsequence can be solved using dynamic programming in O(n^2) time complexity.

The time complexity of the Merge operation in Merge Sort is O(n) as it combines two sorted arrays.

The partitioning step in Quick Sort has a time complexity of O(n) as it involves a single pass through the array.

The average case time complexity of quicksort is O(n log n) due to the divide and conquer approach.