Finding the k-th smallest element using a priority queue requires extracting the minimum k times, resulting in O(k log n) time complexity.

In a max-heap, the value of a parent node must be greater than or equal to its children, so the maximum value of the children can be 20.

The height of a max-heap is log(n) because it is a complete binary tree, where n is the number of elements.

In a min-heap, the parent node is always less than or equal to its children, ensuring the minimum element is at the root.

When the maximum element is removed, the last element is placed at the root, and then the heap is restructured to maintain the heap property.

A binary heap allows for faster deletion of the maximum (or minimum) element compared to an unsorted array, where deletion would take O(n) time.

Building a heap from an array can be done in O(n) time using the heapify process.

Extracting the minimum element from a min-heap requires restructuring the heap, which takes O(log n) time.

Deleting the minimum element from a min-heap involves removing the root and then re-heapifying, which takes O(log n) time.

A priority queue is commonly implemented using a heap, as it allows for efficient insertion and deletion of the highest (or lowest) priority element.

Sorting is not a direct operation of a priority queue; it can be achieved by repeatedly removing elements, but it is not a standard operation.

Heaps can be efficiently implemented using arrays, where the parent-child relationship can be easily calculated.