In the worst case, quicksort can degrade to O(n^2) time complexity, typically when the pivot is the smallest or largest element.

The worst-case time complexity for Quick Sort occurs when the pivot is the smallest or largest element, resulting in O(n^2).

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

Traversing a binary tree requires visiting each node once, leading to a time complexity of O(n).

Traversing a binary tree requires visiting each node once, leading to a time complexity of O(n).

Traversing a linked list requires visiting each node, leading to a time complexity of O(n).

A /18 subnet mask allows for 2^(32-18) = 16384 total addresses, of which 16382 are usable for hosts.

The output of the intermediate code generation phase is typically an intermediate representation that can be further optimized and translated into machine code.

The worst-case number of comparisons is log2(16) = 4, but since we start counting from 0, it takes 5 comparisons.

In the worst case, binary search will check all levels of the tree, which is O(log n), but it will not find the element.

In the worst case, binary search makes log n comparisons to find the target or determine its absence.

In the worst case, binary search will make log n comparisons, where n is the number of elements in the array.

In the worst case, binary search makes log n comparisons, where n is the number of elements in the array.

In the worst case, binary search makes log n comparisons, where n is the number of elements in the array.

The worst-case scenario for binary search is O(log n), as it halves the search space with each iteration.

Accessing an element in a queue implemented using a linked list has a worst-case time complexity of O(n) because you may need to traverse the list.

The worst-case time complexity for balancing an AVL tree after insertion is O(log n).

The worst-case time complexity for bubble sort occurs when the array is sorted in reverse order, resulting in O(n^2).

The worst-case time complexity for deleting a node from a Red-Black tree is O(log n) due to its balanced structure.

The worst-case time complexity for deleting a node from an AVL tree is O(log n) due to the need to maintain balance.

The worst-case time complexity for deleting a node in an AVL tree is O(log n) due to the need to maintain balance.

The worst-case time complexity for deleting an element from an AVL tree is O(log n) due to the need to maintain balance.

The worst-case time complexity for deletion in an AVL tree is O(log n) due to the tree's balanced structure.

Both enqueue and dequeue operations can be performed in constant time, O(1), when using a linked list.

In the worst case, if the tree is unbalanced (like a linked list), the time complexity for insertion can be O(n).

The worst-case time complexity for inserting a node in an AVL tree is O(log n) due to the tree's balanced nature.

Inserting at the beginning of a singly linked list is a constant time operation, O(1).

The worst-case time complexity for inserting an element in a binary search tree is O(n), which occurs when the tree is unbalanced.

The worst-case time complexity for inserting an element in an unbalanced binary tree is O(n), when the tree becomes a linear chain.

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