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Sorting

Sorting. Pseudocode of Insertion Sort. Insertion Sort. To sort array A[0.. n -1], sort A[0.. n -2] recursively and then insert A[ n -1] in its proper place among the sorted A[0.. n -2] Usually implemented bottom up (nonrecursively) Example: Sort 6, 4, 1, 8, 5 6 | 4 1 8 5

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Sorting

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  1. Sorting

  2. Pseudocode of Insertion Sort

  3. Insertion Sort To sort array A[0..n-1], sort A[0..n-2] recursively and then insert A[n-1] in its proper place among the sorted A[0..n-2] • Usually implemented bottom up (nonrecursively) Example: Sort 6, 4, 1, 8, 5 6 | 4 1 8 5 4 6 | 1 8 5 1 4 6 | 8 5 1 4 6 8 | 5 1 4 5 6 8

  4. Analysis of Insertion Sort • Time efficiency Cworst(n) = n(n-1)/2 Θ(n2) Cavg(n) ≈n2/4  Θ(n2) Cbest(n) = n - 1  Θ(n)(also fast on almost sorted arrays) • Space efficiency: in-place • Stability: yes • Best elementary sorting algorithm overall • Binary insertion sort

  5. Merge Sort • Divide: Divide the n element array to be sorted in two sub array of n/2 element each. • Conquer: Sort the two sub array recursively using merge sort • Combine: Merge the two sorted sub array to produce the sorted array

  6. Mergesort • Split array A[0..n-1] in two about equal halves and make copies of each half in arrays B and C • Sort arrays B and C recursively • Merge sorted arrays B and C into array A as follows: • Repeat the following until no elements remain in one of the arrays: • compare the first elements in the remaining unprocessed portions of the arrays • copy the smaller of the two into A, while incrementing the index indicating the unprocessed portion of that array • Once all elements in one of the arrays are processed, copy the remaining unprocessed elements from the other array into A.

  7. Mergesort Example

  8. Pseudocode of Mergesort

  9. Pseudocode of Merge

  10. Analysis of Mergesort • All cases have same efficiency: Θ(n log n) • Number of comparisons in the worst case is close to theoretical minimum for comparison-based sorting: log2n!≈n log2 n - 1.44n

  11. Quick Sort • Divide: The array A[l..r] is partitioned into two nonempty sub array A[l..m] and A[m+1..r] such that each element of A[l..m] is less than or equal to each element of A[m+1..r]. The index m is computed as part of the partitioning process. • Conquer: The two sub array are sorted in place by recursive call to quick sort. • Combine: since the sub arrays are sorted in place, no work is needed to combine them.

  12. p A[i]p A[i]p Quicksort • Select a pivot (partitioning element) – here, the first element • Rearrange the list so that all the elements in the first s positions are smaller than or equal to the pivot and all the elements in the remaining n-s positions are larger than or equal to the pivot • Exchange the pivot with the last element in the first (i.e., ) subarray — the pivot is now in its final position • Sort the two subarrays recursively

  13. Quicksort Example 5 3 1 9 8 2 4 7

  14. Quick Sort Algorithm Quicksort(A[l..r]) If l< r then m = Partition(A[l..r] Quicksort(A[l..m] Quicksort(A[m+1..l)

  15. Partitioning Algorithm

  16. Analysis of Quicksort • Best case: split in the middle — Θ(n log n) • Worst case: sorted array! — Θ(n2) • Average case: random arrays —Θ(n log n) • Improvements: • better pivot selection: median of three partitioning • switch to insertion sort on small subfiles • elimination of recursion These combine to 20-25% improvement • Considered the method of choice for internal sorting of large files (n≥ 10000)

  17. Heap and Heap Sort Definition: A heap is a binary tree with the following conditions: • it is essentially complete: all its levels are full, except last level where only some rightmost leaves may be missing • The key at each node is ≥ keys at its children

  18. Example a heap not a heap not a heap Note: Heap’s elements are ordered top down (along any path down from its root), but they are not ordered left to right

  19. Some Important Properties of a Heap • Given n, there exists a unique binary tree with n nodes that is essentially complete, with h = log2 n • The root contains the largest key • The subtree rooted at any node of a heap is also a heap • A heap can be represented as an array

  20. 1 2 3 4 5 6 9 5 3 1 4 2 Heap’s Array Representation Store heap’s elements in an array (whose elements indexed, for convenience, 1 to n) in top-down left-to-right order Example: • Left child of node j is at 2j • Right child of node j is at 2j+1 • Parent of node j is at j/2 • Parental nodes are represented in the first n/2 locations 9 5 3 1 4 2

  21. Heap Construction (bottom-up) Step 0: Initialize the structure with keys in the order given Step 1: Starting with the last (rightmost) parental node, fix the heap rooted at it, if it doesn’t satisfy the heap condition: keep exchanging it with its largest child until the heap condition holds Step 2: Repeat Step 1 for the preceding parental node

  22. Example of Heap Construction Construct a heap for the list 2, 9, 7, 6, 5, 8

  23. Bottom-up heap construction algorithm

  24. Heap sort Algorithm: • Build heap • Remove root –exchange with last (rightmost) leaf • Fix up heap (excluding last leaf) Repeat 2, 3 until heap contains just one node.

  25. Root deletion The root of a heap can be deleted and the heap fixed up as follows: • exchange the root with the last leaf • compare the new root (formerly the leaf) with each of its children and, if one of them is larger than the root, exchange it with the larger of the two. • continue the comparison/exchange with the children of the new root until it reaches a level of the tree where it is larger than both its children

  26. Example of Sorting by Heapsort Sort the list 2, 9, 7, 6, 5, 8 by heapsort Stage 1 (heap construction) Stage 2 (root/max removal) 2 9 7 6 5 8 9 6 8 2 5 7 2 9 8 6 5 7 7 6 8 2 5 | 9 2 9 8 6 5 7 8 6 7 2 5 | 9 9 2 8 6 5 7 5 6 7 2 | 8 9 9 6 8 2 5 7 7 6 5 2 | 8 9 2 6 5 | 7 8 9 6 2 5 | 7 8 9 5 2 | 6 7 8 9 5 2 | 6 7 8 9 2 | 5 6 7 8 9

  27. Analysis of Heap sort (continued) Recall algorithm: • Build heap • Remove root –exchange with last (rightmost) leaf • Fix up heap (excluding last leaf) Repeat 2, 3 until heap contains just one node. Θ(n) Θ(log n) n – 1 times Total:Θ(n) + Θ( n log n) = Θ(n log n) • Note: this is the worst case. Average case also Θ(n log n).

  28. Priority queues • A priority queue is the ADT of an ordered set with the operations: • find element with highest priority • delete element with highest priority • insert element with assigned priority • Heaps are very good for implementing priority queues

  29. Insertion of a new element • Insert element at last position in heap. • Compare with its parent and if it violates heap condition exchange them • Continue comparing the new element with nodes up the tree until the heap condition is satisfied

  30. Insertion of a New Element into a Heap • Insert the new element at last position in heap. • Compare it with its parent and, if it violates heap condition,exchange them • Continue comparing the new element with nodes up the tree until the heap condition is satisfied Example: Insert key 10 Efficiency: O(log n)

  31. Bottom-up vs. Top-down heap construction • Top down: Heaps can be constructed by successively inserting elements into an (initially) empty heap • Bottom-up: Put everything in and then fix it

  32. Radix Sort • Based on examining digits in some base-b numeric representation of items (or keys) • Least significant digit radix sort • Processes digits from right to left • Used in early punched-card sorting machines • Create groupings of items with same value in specified digit • Collect in order and create grouping with next significant digit

  33. Radix Sort • Sort each digit (or field) separately. • Start with the least-significant digit. • Radix sort must invoke a stable sort. RADIX-SORT(A, d) 1 fori← 1 to d 2 do use a stable sort to sort array A on digit i

  34. Radix Sort in Action

  35. Running Time of Radix Sort • use counting sort as the invoked stable sort, if the range of digits is not large • if digit range is 1..k, then each pass takes Θ(n+k) time • there are d passes, for a total of Θ(d(n+k)) • if k = O(n), time is Θ(dn) • when d is const, we have Θ(n), linear!

  36. Another example • Radix Sort Example • data[ ] • 123 234 345 456 543 987 654 23 76 934 765 452 857 356 805 294 490 780 120 200 73 • Buckets[ ] • 0: 490 780 120 200 • 1: • 2: 452 • 3: 123 543 23 73 • 4: 234 654 934 294 • 5: 345 765 805 • 6: 456 76 356 • 7: 987 857 • 8: • 9:

  37. Another example (Cont.) • data[ ] • 490 780 120 200 452 123 543 23 73 234 654 934 294 345 765 805 456 76 356 987 857 • Buckets[ ] • 0: 200 805 • 1: • 2: 120 123 23 • 3: 234 934 • 4: 345 543 • 5: 452 654 456 356 857 • 6: 765 • 7: 73 76 • 8: 780 987 • 9: 490 294

  38. Another example (Cont.) • data[ ] • 200 805 120 123 23 234 934 345 543 452 654 456 356 857 765 73 76 780 987 490 294 • buckets[ ] • 0: 23 73 76 • 1: 120 123 • 2: 200 234 294 • 3: 345 356 • 4: 452 456 490 • 5: 543 • 6: 654 • 7: 765 780 • 8: 805 857 • 9: 934 987 • data[ ] • 23 73 76 120 123 200 234 294 345 356 452 456 490 543 654 765 780 805 857 934 987

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