1. Introduction to Sorting

2. What is 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 }

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

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

5. Review of Complexity 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.

6. Complexity (cont.) 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

7. ● 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). O(n), Ω (n), & Θ (n)

8. Common Big-Oh’s Time complexityExample O(1) constant Adding to the front of a linked list O(log N ) log Finding an entry in a sorted array O( N ) linear Finding an entry in an unsorted array O( N log N ) n-log-n Sorting n items by ‘divide-and-conquer’ O( N 2 ) quadratic Shortest path between two nodes in a graph O( N 3 ) cubic Simultaneous linear equations

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. 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 Bob Ann Joe Zöe Dan Pat Sa m 90 98 86 75 86 90 original array Bob Ann Joe Zöe Dan Pat Sa m 90 98 86 75 86 90 stably sorted

13. 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 Bob Ann Joe Zöe Dan Pat Sa m 90 98 86 75 86 90 original array Bob Ann Joe Zöe Dan Pat Sa m 90 98 86 75 86 90 unstably sorted

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 2085107 5820107 5720108 578 20 5781020

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

16. Insert Action: i=1 2085107 20 5107 8 temp 8 i = 1, first iteration 8205107---

17. Insert Action: i=2 8205107 820 107 temp 5 5 i = 2, second iteration 88201075 5820107---

18. Insert Action: i=3 5820107 5820 7 temp 10 i = 3, third iteration 5810207---

19. Insert Action: i=4 5810207 581020 5810 20 5881020 7 temp 7 7 7 i = 4, forth iteration 5781020---