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30.18. Sorting Part 2

30.18.1. Sorting Part 2

30.18.1.1. Shellsort

30.18.1.2. Shellsort (2)

void shellsort(Comparable[] A) {
  for (int i=A.length/2; i>2; i/=2) // For each increment
    for (int j=0; j<i; j++)         // Sort each sublist
      inssort2(A, j, i);
  inssort2(A, 0, 1);     // Could call regular inssort here
}

/** Modified Insertion Sort for varying increments */
void inssort2(Comparable[] A, int start, int incr) {
  for (int i=start+incr; i<A.length; i+=incr)
    for (int j=i; (j>=incr) && (A[j].compareTo(A[j-incr]) < 0); j-=incr)
      swap(A, j, j-incr);
}

30.18.1.3. Mergesort

30.18.1.4. Mergesort cost

  • Mergesort cost:
  • Mergsort is also good for sorting linked lists.
  • Mergesort requires twice the space.

30.18.1.5. Quicksort

void quicksort(Comparable[] A, int i, int j) { // Quicksort
  int pivotindex = findpivot(A, i, j);  // Pick a pivot
  swap(A, pivotindex, j);               // Stick pivot at end
  // k will be the first position in the right subarray
  int k = partition(A, i, j-1, A[j]);
  swap(A, k, j);                        // Put pivot in place
  if ((k-i) > 1) quicksort(A, i, k-1);  // Sort left partition
  if ((j-k) > 1) quicksort(A, k+1, j);  // Sort right partition
}
int findpivot(Comparable[] A, int i, int j)
  { return (i+j)/2; }

30.18.1.6. Quicksort Partition

1 / 20 Settings
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When we start the partition function, pivot value 60 has been moved to the right most position.

  1. 760
  2. 61
  3. 572
  4. 883
  5. 854
  6. 425
  7. 836
  8. 737
  9. 488
  10. 609
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30.18.1.7. Quicksort Partition Cost

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30.18.1.8. Quicksort Summary

30.18.1.9. Quicksort Worst Case

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30.18.1.10. .

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30.18.1.11. Quicksort Best Case

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30.18.1.12. .

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30.18.1.13. Quicksort Average Case

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30.18.1.14. Optimizations for Quicksort

  • Better Pivot
  • Inline instead of function calls
  • Eliminate recursion
  • Better algorithm for small sublists: Insertion sort
    • Best: Don't sort small lists at all, do a final Insertion Sort to clean up.

30.18.1.15. Heapsort

0 / 0 Settings
<<<>>>

  1. 730
  2. 61
  3. 572
  4. 883
  5. 604
  6. 425
  7. 836
  8. 727
  9. 488
  10. 859
Created with Raphaël 2.1.2
73
6
57
88
60
72
48
85
42
83
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30.18.1.16. Heapsort Analysis

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30.18.1.17. Binsort

  for (i=0; i<A.length; i++)
    B[A[i]] = A[i];
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30.18.1.18. Radix Sort: Linked List

30.18.1.19. .

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30.18.1.20. Radix Sort: Array

30.18.1.21. Radix Sort Implementation

static void radix(Integer[] A, int k, int r) {
  Integer[] B = new Integer[A.length];
  int[] count = new int[r];     // Count[i] stores number of records with digit value i
  int i, j, rtok;

  for (i=0, rtok=1; i<k; i++, rtok*=r) { // For k digits
    for (j=0; j<r; j++) count[j] = 0;    // Initialize count

    // Count the number of records for each bin on this pass
    for (j=0; j<A.length; j++) count[(A[j]/rtok)%r]++;

    // count[j] will be index in B for last slot of bin j.
    // First, reduce count[0] because indexing starts at 0, not 1
    count[0] = count[0] - 1;
    for (j=1; j<r; j++) count[j] = count[j-1] + count[j];

    // Put records into bins, working from bottom of bin
    // Since bins fill from bottom, j counts downwards
    for (j=A.length-1; j>=0; j--) {
      B[count[(A[j]/rtok)%r]] = A[j];
      count[(A[j]/rtok)%r] = count[(A[j]/rtok)%r] - 1;
    }
    for (j=0; j<A.length; j++) A[j] = B[j]; // Copy B back
  }
}

30.18.1.22. .

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30.18.1.23. Radix Sort Analysis

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30.18.1.24. Empirical Analysis

Sort101001K10K100K1MUpDownInsertion.00023.0070.6664.987381.06744200.04129.05Bubble.00035.0202.25277.9427691.0282068070.64108.69Selection.00039.0120.6972.477356.078000069.7669.58Shell.00034.0080.141.9930.25540.440.79Shell/O.00034.0080.121.9129.05300.360.64Merge.00050.0100.121.6119.32190.830.79Merge/O.00024.0070.101.3117.21970.470.66Quick.00048.0080.111.3715.71620.370.40Quick/O.00031.0060.091.1413.61430.320.36Heap.00050.0110.162.0826.73911.571.56Heap/O.00033.0070.111.6120.83341.011.04Radix/4.00838.0810.797.9979.98087.977.97Radix/8.00799.0440.403.9940.04044.003.99

30.18.1.25. Sorting Lower Bound (1)

  • We would like to know a lower bound for the problem of sorting
  • Sorting is O(nlogn) (average, worst cases) because we know of algorithms with this upper bound.
  • Sorting I/O takes Ω(n) time. You have to look at all records to tell if the list is sorted.
  • We will now prove Ω(nlogn) lower bound for sorting.

30.18.1.26. Sorting Lower Bound (2)

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