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© 2004 Goodrich, Tamassia Heaps1 2 65 79
© 2004 Goodrich, Tamassia Heaps2 Recall Priority Queue ADT (§ 7.1.3) A priority queue stores a collection of entries Each entry is a pair (key, value) Main methods of the Priority Queue ADT insert(k, x) inserts an entry with key k and value x removeMin() removes and returns the entry with smallest key Additional methods min() returns, but does not remove, an entry with smallest key size(), isEmpty() Applications: Standby flyers Auctions Stock market
© 2004 Goodrich, Tamassia Heaps3 Recall Priority Queue Sorting (§ 7.1.4) We can use a priority queue to sort a set of comparable elements Insert the elements with a series of insert operations Remove the elements in sorted order with a series of removeMin operations The running time depends on the priority queue implementation: Unsorted sequence gives selection-sort: O(n 2 ) time Sorted sequence gives insertion-sort: O(n 2 ) time Can we do better? Algorithm PQ-Sort(S, C) Input sequence S, comparator C for the elements of S Output sequence S sorted in increasing order according to C P priority queue with comparator C while S.isEmpty () e S.remove (S. first ()) P.insertItem(e, e) while P.isEmpty() e P.removeMin() S.insertLast(e)
© 2004 Goodrich, Tamassia Heaps4 Heaps (§7.3) A heap is a binary tree storing keys at its nodes and satisfying the following properties: Heap-Order: for every internal node v other than the root, key(v) key(parent(v)) Complete Binary Tree: let h be the height of the heap for i 0, …, h 1, there are 2 i nodes of depth i at depth h 1, the internal nodes are to the left of the external nodes 2 65 79 The last node of a heap is the rightmost node of depth h last node
© 2004 Goodrich, Tamassia Heaps5 Height of a Heap (§ 7.3.1) Theorem: A heap storing n keys has height O(log n) Proof: (we apply the complete binary tree property) Let h be the height of a heap storing n keys Since there are 2 i keys at depth i 0, …, h 1 and at least one key at depth h, we have n 1 2 4 … 2 h 1 1 Thus, n 2 h, i.e., h log n 1 2 2 h 1 1 keys 0 1 h 1 h depth
© 2004 Goodrich, Tamassia Heaps6 Heaps and Priority Queues We can use a heap to implement a priority queue We store a (key, element) item at each internal node We keep track of the position of the last node For simplicity, we show only the keys in the pictures (2, Sue) (6, Mark)(5, Pat) (9, Jeff)(7, Anna)
© 2004 Goodrich, Tamassia Heaps7 Insertion into a Heap (§ 7.3.3) Method insertItem of the priority queue ADT corresponds to the insertion of a key k to the heap The insertion algorithm consists of three steps Find the insertion node z (the new last node) Store k at z Restore the heap-order property (discussed next) 2 65 79 insertion node 2 65 79 1 z z
© 2004 Goodrich, Tamassia Heaps8 Upheap After the insertion of a new key k, the heap-order property may be violated Algorithm upheap restores the heap-order property by swapping k along an upward path from the insertion node Upheap terminates when the key k reaches the root or a node whose parent has a key smaller than or equal to k Since a heap has height O(log n), upheap runs in O(log n) time 2 1 5 79 6 z 1 2 5 79 6 z
© 2004 Goodrich, Tamassia Heaps9 Removal from a Heap (§ 7.3.3) Method removeMin of the priority queue ADT corresponds to the removal of the root key from the heap The removal algorithm consists of three steps Replace the root key with the key of the last node w Remove w Restore the heap-order property (discussed next) 2 65 79 last node w 7 65 9 w new last node
© 2004 Goodrich, Tamassia Heaps10 Downheap After replacing the root key with the key k of the last node, the heap-order property may be violated Algorithm downheap restores the heap-order property by swapping key k along a downward path from the root Upheap terminates when key k reaches a leaf or a node whose children have keys greater than or equal to k Since a heap has height O(log n), downheap runs in O(log n) time 7 65 9 w 5 6 7 9 w
© 2004 Goodrich, Tamassia Heaps11 Updating the Last Node The insertion node can be found by traversing a path of O(log n) nodes Go up until a left child or the root is reached If a left child is reached, go to the right child Go down left until a leaf is reached Similar algorithm for updating the last node after a removal
© 2004 Goodrich, Tamassia Heaps12 Heap-Sort (§2.4.4) Consider a priority queue with n items implemented by means of a heap the space used is O(n) methods insert and removeMin take O(log n) time methods size, isEmpty, and min take time O(1) time Using a heap-based priority queue, we can sort a sequence of n elements in O(n log n) time The resulting algorithm is called heap-sort Heap-sort is much faster than quadratic sorting algorithms, such as insertion-sort and selection-sort
© 2004 Goodrich, Tamassia Heaps13 Vector-based Heap Implementation (§2.4.3) We can represent a heap with n keys by means of a vector of length n 1 For the node at rank i the left child is at rank 2i the right child is at rank 2i 1 Links between nodes are not explicitly stored The cell of at rank 0 is not used Operation insert corresponds to inserting at rank n 1 Operation removeMin corresponds to removing at rank n Yields in-place heap-sort 2 65 79 25697 123450
© 2004 Goodrich, Tamassia Heaps14 Merging Two Heaps We are given two two heaps and a key k We create a new heap with the root node storing k and with the two heaps as subtrees We perform downheap to restore the heap- order property 7 3 58 2 64 3 58 2 64 2 3 58 4 6 7
© 2004 Goodrich, Tamassia Heaps15 We can construct a heap storing n given keys in using a bottom-up construction with log n phases In phase i, pairs of heaps with 2 i 1 keys are merged into heaps with 2 i 1 1 keys Bottom-up Heap Construction (§2.4.3) 2 i 1 2 i 1 1
© 2004 Goodrich, Tamassia Heaps16 Example 1516 12476 2023 25 1516 5 124 11 76 27 2023
© 2004 Goodrich, Tamassia Heaps17 Example (contd.) 25 1516 5 124 11 96 27 2023 15 25 16 4 12 5 6 9 11 23 20 27
© 2004 Goodrich, Tamassia Heaps18 Example (contd.) 7 15 2516 4 125 8 6 911 23 2027 4 15 2516 5 12 7 6 8 911 23 2027
© 2004 Goodrich, Tamassia Heaps19 Example (end) 4 15 2516 5 127 10 6 8 911 23 2027 5 15 2516 7 12 10 4 6 8 911 23 2027
© 2004 Goodrich, Tamassia Heaps20 Analysis We visualize the worst-case time of a downheap with a proxy path that goes first right and then repeatedly goes left until the bottom of the heap (this path may differ from the actual downheap path) Since each node is traversed by at most two proxy paths, the total number of nodes of the proxy paths is O(n) Thus, bottom-up heap construction runs in O(n) time Bottom-up heap construction is faster than n successive insertions and speeds up the first phase of heap-sort
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© 2004 Goodrich, Tamassia Binary Search Trees
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