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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) - PowerPoint PPT Presentation
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Introduction to Sorting
By Saad Malik
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 }
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)
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
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.
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
●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)
10
Common Big-Oh’s
Time complexity Example
O(1) constant Adding to the front of a linked listO(log N) log Finding an entry in a sorted arrayO(N) linear Finding an entry in an unsorted arrayO(N log N) n-log-n Sorting n items by ‘divide-and-conquer’O(N2) quadratic Shortest path between two nodes in a graphO(N3) cubic Simultaneous linear equations
(Binary) Finding 8:
9
50
22
21
8
5
1
(Linear) Finding 8:
9
50
22
21
8
5
1Front
Initial:
Final: 63
http://www.cs.sjsu.edu/faculty/lee/cs146/23FL3Complexity.ppt
Big-Oh to Primary Sorts
● Bubble Sort = n²● Selection Sort = n²● Insertion Sort = n²● Merge Sort = n log(n)●Quick Sort = n log(n)
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 inexecuting 10% of the code
• So, how do we locate the critical 10%?
• software metrics tools• global counters to locate bottlenecks (loop executions, function calls)
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
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
Sam
90
98
98
86
75
86
90
original array
Bob
Ann
Joe
Zöe
Dan
Pat
Sam
90
98
98
86
75
86
90
stably sorted
www.cis.upenn.edu/~matuszek/cit594-2002/ Slides/searching.ppt
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
Sam
90
98
98
86
75
86
90
original array
Bob
Ann
Joe
Zöe
Dan
Pat
Sam
90
98
98
86
75
86
90
unstably sorted
www.cis.upenn.edu/~matuszek/cit594-2002/ Slides/searching.ppt
Selection SortingStep:● 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 20rio.ecs.umass.edu/ece242/slides/lect-sorting.ppt
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
Insert Action: i=1
20 8 5 10 7
20 20 5 10 7
8
temp
8
i = 1, first iteration
8 20 5 10 7---
rio.ecs.umass.edu/ece242/slides/lect-sorting.ppt
Insert Action: i=2
8 20 5 10 7
8 20 20 10 7
temp
5
5
i = 2, second iteration
8 8 20 10 75
5 8 20 10 7---
rio.ecs.umass.edu/ece242/slides/lect-sorting.ppt
Insert Action: i=3
5 8 20 10 7
5 8 20 20 7
temp
10
10
i = 3, third iteration
5 8 10 20 7---
rio.ecs.umass.edu/ece242/slides/lect-sorting.ppt
Insert Action: i=4
5 8 10 20 7
5 8 10 20 20
5 8 10 10 20
5 8 8 10 20
7
temp
7
7
7
i = 4, forth iteration
5 7 8 10 20---
rio.ecs.umass.edu/ece242/slides/lect-sorting.ppt
Sorting Webpage
http://www.cs.ubc.ca/spider/harrison/Java/sorting-demo.html