HeapsortWhy study Heapsort?What is a “heap”?Balanced binary treesLeft-justified binary treesPlan of attackThe heap propertysiftUpConstructing a heap IConstructing a heap IIConstructing a heap IIIOther children are not affectedA sample heapRemoving the rootThe reHeap method IThe reHeap method IIThe reHeap method IIIThe reHeap method IVSortingMapping into an arrayRemoving and replacing the rootReheap and repeatAnalysis IAnalysis IIAnalysis IIIAnalysis IVThe EndHeapsortWhy study Heapsort?•It is a well-known, traditional sorting algorithm you will be expected to know•Heapsort is always O(n log n)–Quicksort is usually O(n log n) but in the worst case slows to O(n2)–Quicksort is generally faster, but Heapsort is better in time-critical applications•Heapsort is a really cool algorithm!What is a “heap”?•Definitions of heap:1. A large area of memory from which the programmer can allocate blocks as needed, and deallocate them (or allow them to be garbage collected) when no longer needed2. A balanced, left-justified binary tree in which no node has a value greater than the value in its parent•These two definitions have little in common•Heapsort uses the second definitionBalanced binary trees•Recall:–The depth of a node is its distance from the root–The depth of a tree is the depth of the deepest node•A binary tree of depth n is balanced if all the nodes at depths 0 through n-2 have two childrenBalanced Balanced Not balancedn-2n-1nLeft-justified binary trees•A balanced binary tree is left-justified if:–all the leaves are at the same depth, or–all the leaves at depth n+1 are to the left of all the nodes at depth nLeft-justified Not left-justifiedPlan of attack•First, we will learn how to turn a binary tree into a heap•Next, we will learn how to turn a binary tree back into a heap after it has been changed in a certain way•Finally (this is the cool part) we will see how to use these ideas to sort an arrayThe heap property•A node has the heap property if the value in the node is as large as or larger than the values in its children•All leaf nodes automatically have the heap property•A binary tree is a heap if all nodes in it have the heap property128 3Blue node has heap property128 12Blue node has heap property128 14Blue node does not have heap propertysiftUp•Given a node that does not have the heap property, you can give it the heap property by exchanging its value with the value of the larger child•This is sometimes called sifting up•Notice that the child may have lost the heap property148 12Blue node has heap property128 14Blue node does not have heap propertyConstructing a heap I•A tree consisting of a single node is automatically a heap•We construct a heap by adding nodes one at a time:–Add the node just to the right of the rightmost node in the deepest level–If the deepest level is full, start a new level•Examples:Add a new node hereAdd a new node hereConstructing a heap II•Each time we add a node, we may destroy the heap property of its parent node•To fix this, we sift up•But each time we sift up, the value of the topmost node in the sift may increase, and this may destroy the heap property of its parent node•We repeat the sifting up process, moving up in the tree, until either–We reach nodes whose values don’t need to be swapped (because the parent is still larger than both children), or–We reach the rootConstructing a heap III8 810108108 5108 5121012 581210 581 2 34Other children are not affected•The node containing 8 is not affected because its parent gets larger, not smaller•The node containing 5 is not affected because its parent gets larger, not smaller•The node containing 8 is still not affected because, although its parent got smaller, its parent is still greater than it was originally1210 58 141214 58 101412 58 10A sample heap•Here’s a sample binary tree after it has been heapified•Notice that heapified does not mean sorted•Heapifying does not change the shape of the binary tree; this binary tree is balanced and left-justified because it started out that way191418223211411915251722Removing the root•Notice that the largest number is now in the root•Suppose we discard the root:•How can we fix the binary tree so it is once again balanced and left-justified?•Solution: remove the rightmost leaf at the deepest level and use it for the new root191418223211411915172211The reHeap method I•Our tree is balanced and left-justified, but no longer a heap•However, only the root lacks the heap property•We can siftUp() the root•After doing this, one and only one of its children may have lost the heap property1914182232114915172211The reHeap method II•Now the left child of the root (still the number 11) lacks the heap property•We can siftUp() this node•After doing this, one and only one of its children may have lost the heap property1914182232114915171122The reHeap method III•Now the right child of the left child of the root (still the number 11) lacks the heap property:•We can siftUp() this node•After doing this, one and only one of its children may have lost the heap property —but it doesn’t, because it’s a leaf1914181132114915172222The reHeap method IV•Our tree is once again a heap, because every node in it has the heap property•Once again, the largest (or a largest) value is in the root•We can repeat this process until the tree becomes empty•This produces a sequence of values in order largest to smallest1914182131114915172222Sorting•What do heaps have to do with sorting an array?•Here’s the neat part:–Because the binary tree is balanced and left justified, it can be represented as an array–All our operations on binary trees can be represented as operations on arrays–To sort: heapify the array; while the array isn’t empty { remove and replace the root; reheap the new root node;}Mapping into an array•Notice:–The left child of index i is at index 2*i+1–The right child of index i is at index 2*i+2–Example: the children of node 3 (19) are 7 (18) and 8 (14)19141822321141191525172225 22 17 19 22 14 15 18 14 21 3 9 11 0 1 2 3 4 5 6 7 8 9 10 11 12Removing and replacing the root•The “root” is the first element in the array•The “rightmost node at the deepest level” is the last element•Swap them...•...And pretend that the last element in the array no longer exists—that is, the