# PartD 1 Priority Queues 1 Priority Queue ADT

• Slides: 29

Part-D 1 Priority Queues 1

Priority Queue ADT A priority queue stores a collection of entries Each entry is a pair (key, value) Main methods of the Priority Queue ADT n n insert(k, x) inserts an entry with key k and value x remove. Min() removes and returns the entry with smallest key Priority Queues Additional methods n n min() returns, but does not remove, an entry with smallest key size(), is. Empty() Applications: n n n Standby flyers Auctions Stock market 2

Entry ADT An entry in a priority queue is simply a keyvalue pair Priority queues store entries to allow for efficient insertion and removal based on keys Methods: n n key(): returns the key for this entry value(): returns the value associated with this entry As a Java interface: /** * Interface for a key-value * pair entry **/ public interface Entry { public Object key(); public Object value(); } Priority Queues 3

Comparator ADT A comparator encapsulates the action of comparing two objects according to a given total order relation A generic priority queue uses an auxiliary comparator The comparator is external to the keys being compared When the priority queue needs to compare two keys, it uses its comparator The primary method of the Comparator ADT: n Priority Queues compare(a, b): Returns an integer i such that i < 0 if a < b, i = 0 if a = b, and i > 0 if a > b; an error occurs if a and b cannot be compared. 4

Priority Queue Sorting We can use a priority queue to sort a set of comparable elements 1. Insert the elements one by one with a series of insert operations 2. Remove the elements in sorted order with a series of remove. Min operations The running time of this sorting method depends on the priority queue implementation 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. is. Empty () e S. remove. First () P. insert (e, 0) while P. is. Empty() e P. remove. Min(). key() S. insert. Last(e) Priority Queues 5

Sequence-based Priority Queue Implementation with an unsorted list Implementation with a sorted list 4 1 5 2 3 1 Performance: n n insert takes O(1) time since we can insert the item at the beginning or end of the sequence remove. Min and min take O(n) time since we have to traverse the entire sequence to find the smallest key Priority Queues 2 3 4 5 Performance: n n insert takes O(n) time since we have to find the place where to insert the item remove. Min and min take O(1) time, since the smallest key is at the beginning 6

Selection-Sort Selection-sort is the variation of PQ-sort where the priority queue is implemented with an unsorted list Running time of Selection-sort: 1. Inserting the elements into the priority queue with n insert operations takes O(n) time 2. Removing the elements in sorted order from the priority queue with n remove. Min operations takes time proportional to 1 + 2 + …+ n-1 Selection-sort runs in O(n 2) time Priority Queues 7

Selection-Sort Example Input: Sequence S (7, 4, 8, 2, 5, 3, 9) Priority Queue P () Phase 1 (a) (b). . . (g) (4, 8, 2, 5, 3, 9) (8, 2, 5, 3, 9). . . () (7, 4) Phase 2 (a) (b) (c) (d) (e) (f) (g) (2, 3) (2, 3, 4, 5) (2, 3, 4, 5, 7, 8) (2, 3, 4, 5, 7, 8, 9) (7, 4, 8, 5, 3, 9) (7, 4, 8, 5, 9) (7, 8, 9) (9) () Priority Queues Running time: 1+2+3+…n 1=O(n 2). No advantage is shown by (7, 4, 8, 2, 5, 3, 9) using unsorted list. 8

Heaps A heap is a binary tree storing keys at its nodes and satisfying the following properties: n n Heap-Order: for every node v other than the root, key(v) key(parent(v)) Complete Binary Tree: let h be the height of the heap w for i = 0, … , h - 1, there are 2 i nodes of depth i w at depth h - 1, the internal nodes are to the left of the external nodes A full binary without the last few nodes at the bottom on the right. Priority Queues The last node of a heap is the rightmost node of depth h Root has the smallest key 2 5 9 6 7 last node 9

Complete Binary Trees Once the number of nodes in the complete binary tree is fixed, the tree is fixed. For example, a complete binary tree of 15 node is shown in the slide. A CBT of 14 nodes is the one without node 8. 1 2 3 4 5 6 7 8 A CBT of 13 node is the one without nodes 7 and 8. A binary tree of height 3 has the first p leaves at the bottom for p=1, 2, …, 8=23. Priority Queues 10

Height of a Heap 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=2 h. Thus, n 2 h , i. e. , h log n n depth keys 0 1 1 2 h-1 2 h-1 h 1 Priority Queues 11

Heaps and Priority Queues We We We For can use a heap to implement a priority queue store a (key, element) item at each internal node keep track of the position of the last node simplicity, we show only the keys in the pictures (2, Sue) (5, Pat) (9, Jeff) (6, Mark) (7, Anna) Priority Queues 12

Insertion into a Heap Method insert. Item of the priority queue ADT corresponds to the insertion of a key k to the heap The insertion algorithm consists of three steps n n n Create the node z (the new last node). Store k at z Put z as the last node in the complete binary tree. Restore the heap-order property, i. e. , upheap (discussed next) 2 5 9 6 z 7 insertion node 2 5 9 Priority Queues 6 7 z 1 13

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 9 1 7 z 6 5 9 Priority Queues 2 7 z 6 14

An example of upheap 1 2 4 9 5 10 2 3 11 6 14 15 7 16 17 2 2 3 7 Different entries may have the same key. Thus, a key may appear more than once. Priority Queues 15

Removal from a Heap Method remove. Min of the priority queue ADT corresponds to the removal of the root key from the heap The removal algorithm consists of three steps n n n 2 5 9 7 w last node 7 Replace the root key with the key of the last node w Remove w Restore the heap-order property. e. , downheap(discussed next) 6 5 w 6 9 Priority Queues new last node 16

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 (always use the child with smaller key) from the root Downheap 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 5 5 6 7 9 6 9 Priority Queues 17

An example of downheap 2 4 17 2 3 9 17 4 17 9 17 5 10 11 6 14 15 7 16 Priority Queues 18

Priority Queue ADT using a heap A priority queue stores a collection of entries Each entry is a pair (key, value) Main methods of the Priority Queue ADT n n Additional methods insert(k, x) inserts an entry with key k and value x O(log n) remove. Min() removes and returns the entry with smallest key O(log n) Priority Queues n min() returns, but does not remove, an entry with smallest key O(1) n size(), is. Empty() O(1) Running time of Size(): when constructing the heap, we keep the size in a variable. When inserting or removing a node, we update the value of the variable in O(1) time. is. Empty(): takes O(1) time using size(). (if size==0 then …) 19

Heap-Sort Consider a priority queue with n items implemented by means of a heap n n n the space used is O(n) methods insert and remove. Min take O(log n) time methods size, is. Empty, 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 Priority Queues 20

Vector-based Heap Implementation We can represent a heap with n keys by means of an array of length n + 1 For the node at rank i n n the left child is at rank 2 i the right child is at rank 2 i + 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 remove. Min corresponds to removing at rank n Yields in-place heap-sort The parent of node at rank i is i/2 Priority Queues 2 5 6 9 0 7 2 5 6 9 7 1 2 3 4 5 21

Merging Two Heaps We are given 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 heaporder property 3 8 2 5 4 6 7 3 8 2 5 4 6 2 3 8 Priority Queues 4 5 7 6 22

Bottom-up Heap Construction (§ 2. 4. 3) 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 Priority Queues 2 i -1 2 i+1 -1 23

Example 16 15 4 25 16 12 6 5 15 4 7 23 11 12 Priority Queues 6 20 27 7 23 20 24

Example (contd. ) 25 16 5 15 4 15 16 11 12 6 4 25 5 27 9 23 6 12 11 Priority Queues 20 20 9 23 27 25

Example (contd. ) 7 8 15 16 4 25 5 6 12 11 20 9 4 5 25 27 6 15 16 23 7 8 12 11 Priority Queues 20 9 23 27 26

Example (end) 10 4 6 15 16 5 25 7 8 12 11 20 9 23 27 4 5 6 15 16 7 25 10 8 12 11 Priority Queues 20 9 23 27 27

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 Priority Queues 28

Part-E Hash Table This time, it is O. k. Perhaps, I should put O(n) heap construction part into next time. For Hash Table, delete some methods. Introduce ONE method for each issue. My students do not like too many methods at a time. Priority Queues 29