priority queue and binary heap neil tang 02/09/2010
DESCRIPTION
Priority Queue and Binary Heap Neil Tang 02/09/2010. Class Overview. Priority queue Binary heap Heap operations: insert, deleteMin, de/increaseKey, delete, buildHeap Application. Priority Queue. A priority queue is a queue in which each element has a - PowerPoint PPT PresentationTRANSCRIPT
CS223 Advanced Data Structures and Algorithms 1
Priority Queue and Binary HeapPriority Queue and Binary Heap
Neil TangNeil Tang02/09/201002/09/2010
CS223 Advanced Data Structures and Algorithms 2
Class OverviewClass Overview
Priority queue
Binary heap
Heap operations: insert, deleteMin, de/increaseKey, delete, buildHeap
Application
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Priority QueuePriority Queue
A priority queue is a queue in which each element has a
priority and elements with higher priorities are supposed to
be removed before the elements with lower priorities.
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Possible SolutionsPossible Solutions
Linked list: Insert at the front (O(1)) and traverse the list to delete (O(N)).
Linked list: Keep it always sorted. traverse the list to insert (O(N)) and delete the first element (O(1)).
Binary search tree
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Binary HeapBinary Heap
A binary heap is a binary tree that is completely filled, with possible exception of the bottom level and in which for every node X, the key in the parent of X is smaller than (or equal to) the key in X.
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Binary HeapBinary Heap
A complete binary tree of height h has between 2h and 2h+1 -1 nodes. So h = logN.
For any element in array position i, its left child in position 2i and the right child is in position (2i+1), and the parent is in i/2.
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Insert 14Insert 14
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Insert (Percolate Up)Insert (Percolate Up)
Time complexity: O(logN)
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deleteMindeleteMin
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deleteMin deleteMin (Percolate Down)(Percolate Down)
Time complexity: O(logN)
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Other OperationsOther Operations
decreaseKey(p,)
increaseKey(p, )
delete(p)?
delete(p)=decreaseKey(p,)+deleteMin()
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buildHeapbuildHeap
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buildHeapbuildHeap
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buildHeapbuildHeap
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buildHeapbuildHeap
Theorem: For the perfect binary tree of height h with (2h+1-1) nodes the sum of the heights of the nodes is (2h+1-1-(h+1)).
Time complexity: 2h+1-1-(h+1) = O(N).
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ApplicationsApplications
Problem: find the kth smallest element.
Algorithm: buildHeap, then deleteMin k times.
Time complexity: O(N+klogN) = O(NlogN).
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ApplicationsApplications
Problem: find the kth largest element.
Algorithm: buildHeap with the first k elements, check the rest one by one. In each step, if the new element is larger than the element in the root node, deleteMin and insert the new one.
Time complexity: O(k+(N-k)logk) = O(NlogN).