Learn about heap sort, its algorithm and working. Learn the heap data structure, heapify algorithm and implementation.

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Learn about heap sort, its algorithm and working. Learn the heap data structure, heapify algorithm and implementation.
Heap Sort
Heap sort tutorial.
Quicksort Program and Complexity (Big-O)
Quicksort Program and Complexity (Big-O)
Quicksort is a comparison sort based on divide and conquer algorithm. Quick sort is more fast in comparison to Merge Sort ot Heap Sort. It’s not required additional space for sorting.
How Quick Sort Works
The idea to implement Quicksort is first divides a large array into two smaller sub-arrays as the low elements and the high elements then recursively sort the sub-arrays.
The above process…
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Performance Analysis of Four Different Types of Sorting Algorithms using Different Languages
By Dr. I. Lakshmi"Performance Analysis of Four Different Types of Sorting Algorithms using Different Languages"
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018,
URL: http://www.ijtsrd.com/papers/ijtsrd9464.pdf
http://www.ijtsrd.com/computer-science/other/9464/performance-analysis-of-four-different-types-of-sorting-algorithms-using-different-languages/dr-i-lakshmi
peer reviewed journal, call for paper technology, social science journal
Understanding that in what condition, which sorting algorithm should be used
Understanding that in what condition, which sorting algorithm should be used.
http://www.knowsh.com Understanding that in what condition, which sorting algorithm should be used. Comparison between :- Quick Sort, Merge Sort, Heap Sort, Introsort, Insertion Sort, Bubble Sort, Selection Sort, Counting Sort, Radix Sort & Bucket Sort. http://knowsh.com/Notes/210295/When-To-Use-Which-Sorting
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Weekend Learning
Topics
Radix Sorting - bucket sorting
LSD - Least Significant Digit (start from ones digit, move <--)
MSD - Most Significant Digit (start from left digit, move -->)
Use nested buckets - MSD for 3-digit numbers ex:
Start with hundreds-place-digit % 10 bucket
Within that bucket, place in tens-place-digit % 10 bucket
Within that bucket, place in ones-place-digit % 10 bucket
Can mod by different size/number of buckets
Will take O(n) time to place elements - placing in sorted order
Will take O(n) time to retrieve elements in sorted order
Total sort time is O(n) time
Not a comparative sort, since merely placing elements
Can only work with arrays of integers
Good for when you have a set range of integes since sort is very fast
Bad for an infinite/unknown range, since can't set the appropriate number of buckets
Heaps - Binary tree where each parent is smaller than its children
Min Heap - default, what most people assume when talking about Heaps
Max Heap - binary tree where each parent is larger than its children
Often stored as an array where reading in a Breadth First manner
[1,2,3,4,5,6,7]
1 - root
2, 3 -- children of 1
4, 5 -- children of 2
6, 7 -- children of 3
Can reference parents/children by index
Parent of node i is node (i-1)/2
Children of node i are (i*2)+1 & (i*2)+2
O(n) time to place all elememnts
Heap Sort
Percolation - can either percolate up or percolate down
Percolating up - add a new element on the end of the array (become new leaf of tree), then compare it to its parent (using index). If it is smaller than its parent, swap positions. Compare to new parent. Continue this until new element is smaller than its parent (in correct position)
Percolating down - add a new element to the beginning of the array (make it the new root). Of its two children, swap with the smaller of the two (if it's smaller. Check new children, and swap with the smaller of the two if it's still larger. Continue this until it is larger than its children.
Because we're storing a binary tree in an array, it will compact to the top (so no extra-long branches)
Number of levels for n nodes --> log(n)
Hence, will take max O(logn) time to percolate up or percolate down
Because it takes O(logn) time to place 1 element, it will take O(nlogn) time to place all objects into the binary tree. Once placed, elements are in sorted order
Take first element, place at end of array and percolate up. This will take max O(logn) time
O(nlogn) time to sort all elements
In-place algorithm
Not a stable sort
Chat with Nicholas Neumann-Chun
Synthesizer Saturday: The Sounds Of Sorting Algorithms Sounds Of Sorting Algorithms is a project by Casey Rule that generates musical tones in an order determined by selecting a…
Understanding Heap Sort
[nextpage title=”Introduction”] Heap Sort is one of the efficient sorting algorithms with a guaranteed worst case running time of O(NlogN). This is an in place sorting algorithm but it does not offer stable sorting.
In place Sorting: A sorting algorithm which sorts the input using very small or no extra space. It means that the input data is overwritten by the resulting output data.
Stable…
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