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Introduction to Sorting

By Saad Malik. Introduction to Sorting. Sorting: an operation that segregates items into groups according to specified criterion. A = { 3 1 6 2 1 3 4 5 9 0 } A = { 0 1 1 2 3 3 4 5 6 9 }. What is Sorting?. Consider : Sorting Books in Library (Dewey system)

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Introduction to Sorting

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  1. By Saad Malik Introduction to Sorting

  2. Sorting: an operation that segregates items into groups according to specified criterion. A = { 3 1 6 2 1 3 4 5 9 0 } A = { 0 1 1 2 3 3 4 5 6 9 } What is Sorting?

  3. Consider: Sorting Books in Library (Dewey system) Sorting Individuals by Height (Feet and Inches) Sorting Movies in Blockbuster (Alphabetical) Sorting Numbers (Sequential) Why Sort and Examples

  4. There are many, many different types of sorting algorithms, but the primary ones are: Types of Sorting Algorithms • Bubble Sort • Selection Sort • Insertion Sort • Merge Sort • Shell Sort • Heap Sort • Quick Sort • Radix Sort • Swap Sort

  5. Most of the primary sorting algorithms run on different space and time complexity. Time Complexity is defined to be the time the computer takes to run a program (or algorithm in our case). Space complexity is defined to be the amount of memory the computer needs to run a program. Review of Complexity

  6. Complexity in general, measures the algorithms efficiency in internal factors such as the time needed to run an algorithm. External Factors (not related to complexity): Size of the input of the algorithm Speed of the Computer Quality of the Compiler Complexity (cont.)

  7. O(n), Ω(n), & Θ(n) • An algorithm or function T(n) is O(f(n)) whenever T(n)'s rate of growth is less than or equal to f(n)'s rate. • An algorithm or function T(n) is Ω(f(n)) whenever T(n)'s rate of growth is greater than or equal to f(n)'s rate. • An algorithm or function T(n) is Θ(f(n)) if and only if the rate of growth of T(n) is equal to f(n).

  8. Time complexity Example constant O(1) Adding to the front of a linked list N log O(log ) Finding an entry in a sorted array N linear O( ) Finding an entry in an unsorted array N N n-log-n O( log ) Sorting n items by ‘divide-and-conquer’ 2 N quadratic O( ) Shortest path between two nodes in a graph 3 N cubic O( ) Simultaneous linear equations 1 1 5 5 8 8 (Binary) Finding 8: (Linear) Finding 8: 9 9 21 21 22 22 50 50 Common Big-Oh’s Front Initial: 1 0 Final: 6 3 http://www.cs.sjsu.edu/faculty/lee/cs146/23FL3Complexity.ppt

  9. Big-Oh to Primary Sorts • Bubble Sort = n² • Selection Sort = n² • Insertion Sort = n² • Merge Sort = n log(n) • Quick Sort = n log(n)

  10. Time Efficiency • How do we improve the time efficiency of a program? • The 90/10 Rule 90% of the execution time of a program is spent in executing 10% of the code • So, how do we locate the critical 10%? • software metrics tools • global counters to locate bottlenecks (loop executions, function calls)

  11. Time Efficiency Improvements Possibilities (some better than others!) • Move code out of loops that does not belong there (just good programming!) • Remove any unnecessary I/O operations (I/O operations are expensive time-wise) • Code so that the compiled code is more efficient Moral - Choose the most appropriate algorithm(s) BEFORE program implementation

  12. Ann 98 Ann 98 Bob 90 Joe 98 Dan 75 Bob 90 Joe 98 Sam 90 Pat 86 Pat 86 Sam 90 Zöe 86 Zöe 86 Dan 75 original array stably sorted Stable sort algorithms • A stable sort keeps equal elements in the same order • This may matter when you are sorting data according to some characteristic • Example: sorting students by test scores www.cis.upenn.edu/~matuszek/cit594-2002/ Slides/searching.ppt

  13. Ann 98 Joe 98 Bob 90 Ann 98 Dan 75 Bob 90 Joe 98 Sam 90 Pat 86 Zöe 86 Sam 90 Pat 86 Zöe 86 Dan 75 original array unstably sorted Unstable sort algorithms • An unstable sort may or may not keep equal elements in the same order • Stability is usually not important, but sometimes it is important www.cis.upenn.edu/~matuszek/cit594-2002/ Slides/searching.ppt

  14. Selection Sorting Step: • 1. select the smallest element • among data[i]~ data[data.length-1]; • 2. swap it with data[i]; • 3. if not finishing, repeat 1&2 20 8 5 10 7 5 8 20 10 7 5 7 20 10 8 5 7 8 10 20 5 7 8 10 20 rio.ecs.umass.edu/ece242/slides/lect-sorting.ppt

  15. Pseudo-code for Insertion Sorting • Place ith item in proper position: • temp = data[i] • shift those elements data[j] which greater than temp to right by one position • place temp in its proper position rio.ecs.umass.edu/ece242/slides/lect-sorting.ppt

  16. Insert Action: i=1 temp 8 20 8 5 10 7 i = 1, first iteration 8 20 20 5 10 7 --- 8 20 5 10 7 rio.ecs.umass.edu/ece242/slides/lect-sorting.ppt

  17. Insert Action: i=2 temp 5 8 20 5 10 7 i = 2, second iteration 5 8 20 20 10 7 5 8 8 20 10 7 --- 5 8 20 10 7 rio.ecs.umass.edu/ece242/slides/lect-sorting.ppt

  18. Insert Action: i=3 temp 10 5 8 20 10 7 i = 3, third iteration 10 5 8 20 20 7 --- 5 8 10 20 7 rio.ecs.umass.edu/ece242/slides/lect-sorting.ppt

  19. Insert Action: i=4 temp 7 5 8 10 20 7 i = 4, forth iteration 7 5 8 10 20 20 7 5 8 10 10 20 7 5 8 8 10 20 --- 5 7 8 10 20 rio.ecs.umass.edu/ece242/slides/lect-sorting.ppt

  20. Sorting Webpage http://www.cs.ubc.ca/spider/harrison/Java/sorting-demo.html

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