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Heapsort Algorithm. CS 583 Analysis of Algorithms. Outline. Sorting Problem Heaps Definition Maintaining heap property Building a heap Heapsort Algorithm. Sorting Problem. Sorting is usually performed not on isolated data, but records .

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Heapsort Algorithm


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    1. Heapsort Algorithm CS 583 Analysis of Algorithms CS583 Fall'06: Heapsort

    2. Outline • Sorting Problem • Heaps • Definition • Maintaining heap property • Building a heap • Heapsort Algorithm CS583 Fall'06: Heapsort

    3. Sorting Problem • Sorting is usually performed not on isolated data, but records. • Each record contains a key, which is the value to be sorted. • The remainder is called satellite data. • When a sorting algorithm permutes the keys, it must permute the satellite data as well. • If the satellite data is large for each record, we often permute pointers to records. • This level of detail is usually irrelevant in the study of algorithms, but is important when converting an algorithm to a program. CS583 Fall'06: Heapsort

    4. Sorting Problem: Importance • Sorting is arguably the most fundamental problem in the study of algorithms for the following reasons: • The need to sort information is often a key part of an application. For example, sorting the financial reports by security IDs. • Algorithms often use sorting as a key subroutine. For example, in order to match a security against benchmarks, the latter set needs to be sorted by some key elements. • There is a wide variety of sorting algorithms, and they use a rich set of techniques. CS583 Fall'06: Heapsort

    5. Heaps • Heapsort algorithm sorts in place and its running time is O(n log(n)). • It combines the better attributes of insertion sort and merge sort algorithms. • It is based on a data structure, -- heaps. • The (binary) heap data structure is an array object that can be viewed as a nearly complete binary tree. • An array A that represents a heap is an object with two attributes: • length[A], which is the number of elements, and • heap-size[A], the number of elements in the heap stored within the array A. CS583 Fall'06: Heapsort

    6. Heaps: Example • A = {10, 8, 6, 5, 7, 3, 2} • 10 • 8 6 • 7 3 2 • The root of the tree is A[1]. Children of a node i determined as follows: • Left(i) • return 2i • Right(i) • return 2i+1 CS583 Fall'06: Heapsort

    7. Heaps: Example (cont.) • The above is proven by induction: • The root's left child is 2 = 2*1. • Assume it is true for node n. • The left child of a node (n+1) will follow the right child of node n: left(n+1) = 2*n + 1 + 1 = 2(n+1) • The parent of a node i is calculated from i=2p, or i=2p+1, where p is a parent node. Hence • Parent(i) • returnfloor(i/2) CS583 Fall'06: Heapsort

    8. Max-Heaps • In a max-heap, for every node i other than the root:  • A[Parent(i)] >= A[i] • For the heapsort algorithm, we use max-heaps. • The height of the heap is defined to be the longest path from the root to a leaf, and it is (lg n) since it is a complete binary tree. • We will consider the following basic procedures on the heap:  • Max-Heapify to maintain the max-heap property. • Build-Max-Heap to produce a max-heap from an unordered input array. • Heapsort to sort an array in place. CS583 Fall'06: Heapsort

    9. Maintaining the Heap Property • The Max-Heapify procedure takes an array A and its index i. • It is assumed that left and right subtrees are already max-heaps. • The procedure lets the value of A[i] "float down" in the max-heap so that the subtree rooted at index i becomes a max-heap. CS583 Fall'06: Heapsort

    10. Max-Heapify (A, i) 1 l = Left(i) 2 r = Right(i) 3 if l <= heap-size[A] and A[l] > A[i] 4 largest = l 5 else 6 largest = i 7 if r <= heap-size[A] and A[r] > A[largest] 8 largest = r 9 if largest <> i 10 <exchange A[i] with A[largest]> 11 Max-Heapify(A, largest) Max-Heapify: Algorithm CS583 Fall'06: Heapsort

    11. It takes (1) to find A[largest], plus the time to run the procedure recursively on at most 2n/3 elements. (This is the maximum size of a child tree. It occurs when the last row of the tree is exactly half full.) Assume there n nodes and x levels in the tree that has half of the last row. This means: n = 1 + 2 + ... + 2^(x-1) + 2^x/2 2^x – 1 + 2^x/2 = n 2^(x-1) = a => 2a + a = n+1 => 2^(x-1) = (n+1)/3 Max-Heapify: Analysis CS583 Fall'06: Heapsort

    12. Max-Heapify: Analysis (cont.) Max subtree size = (half of all elements to level x-1) + (elements at the last level) – (1 root element) = (2^x – 1)/2 + 2^x/2 – 1 = 2^(x-1) – ½ + 2^(x-1) – 1 = n/3 + 1/3 + n/3 + 1/3 – 1.5 = 2n/3 + 2/3 – 1.5 ~ 2n/3 Therefore the running time of Max-Heapify is described by the following recurrence: T(n) <= T(2n/3) + (1) According to the master theorem: T(n) = (lg n) (a=1, b=3/2, f(n) = (1)) Since T(n) is the worst-case scenario, we have a running time of the algorithm at O(lg n). CS583 Fall'06: Heapsort

    13. Building a Heap • We can use the procedure Max-Heapify in a bottom-up manner to convert the whole array A[1..n] into a max-heap. • Note that, elements A[floor(n/2)+1..n] are leaves. The last element that is not a leaf is a parent of the last node, -- floor(n/2). • The procedure Build-Max-Heap goes through all non-leaf nodes and runs Max-Heapify on each of them. CS583 Fall'06: Heapsort

    14. Build-Max-Heap: Algorithm • Build-Max-Heap(A, n) • 1 heap-size[A] = n • 2 for i = floor(n/2) to 1 • 3 Max-Heapify(A,i) • Invariant: • At the start of each iteration 2-3, each node i+1, ... , n is the root of a max-heap. • Proof. • Initialization: i=floor(n/2). Each node in floor(n/2)+1,...,n are leaves and hence are roots of trivial max-heaps. CS583 Fall'06: Heapsort

    15. Build-Max-Heap: Correctness • Maintenance: children of node i are numbered higher than i, and by the loop invariant are assumed to be roots of max-heaps. • This is the condition for Max-Heapify. • Moreover, the Max-Heapify preserves the property that i+1, ... , n are roots of max-heaps. • Decrementing i by 1 makes the loop invariant for the next iteration. • Termination: i=0, hence each node 1,2,...,n is the root of a max-heap. CS583 Fall'06: Heapsort

    16. Build-Max-Heap: Performance • Each call to Max-Heapify takes O(lg n) time and there are n such calls. • Therefore the running time of Build-Max-Heap is O(n lgn). • To derive a tighter bound, we observe that the running time of Max-Heapify depends on the node's height. • An n-element heap has height floor(lgn). There are at most ceil(n/2^(h+1)) nodes of any height h. Assume these nodes are at height x of the original tree. Then we have: CS583 Fall'06: Heapsort

    17. Build-Max-Heap: Performance (cont.) 1+2+...+2^x+...+2^h = n 2^(x+h+1) = n+1 2^x = (n+1)/2^(h+1) = ceil(n/2^(h+1)) The time required by Max-Heapify when called on a node of height h is O(h). Hence: h=0,floor(lgn)ceil(n/2^(h+1)) O(h) = O(nh=0,floor(lgn)h/2^h) A.8: k=0,k/x^k = x/(1-x)^2 h=0,h/2^h = ½ / (1-1/2)^2 = 2 Thus, the running time of Build-Max-Heap can be bounded O(n h=0,floor(lgn)h/2^h) = O(nh=0,h/2^h) = O(n) CS583 Fall'06: Heapsort

    18. The Heapsort Algorithm The heapsort algorithm uses Build-Max-Heap on A[1..n]. Since the maximum element of the array is at A[1], it can be put into correct position A[n]. Now A[1..(n-1)] can be made max-heap again. Heapsort (A,n) 1 Build-Max-Heap(A,n) 2 for i = n to 2 3 <swap A[1] with A[i]> 4 heap-size[A] = heap-size[A]-1 5 Max-Heapify(A,1) Step 1 takes O(n) time. Loop 2 is repeated (n-1) times with step 5 taking most time O(lgn). Hence the running time of heapsort is O(n) + O(n lgn) = O(n lgn). CS583 Fall'06: Heapsort