O(1)
You would sort the given elements of an array by a bubble sort or heap sort code!!
+ reasonable fast in worst and average cases, n lg n + O(n) + in place - best case still n lg n
The heap sort algorithm is as follows: 1. Call the build_max_heap() function. 2. Swap the first and last elements of the max heap. 3. Reduce the heap by one element (elements that follow the heap are in sorted order). 4. Call the sift_down() function. 5. Goto step 2 unless the heap has one element. The build_max_heap() function creates the max heap and takes linear time, O(n). The sift_down() function moves the first element in the heap into its correct index, thus restoring the max heap property. This takes O(log(n)) and is called n times, so takes O(n * log(n)). The complete algorithm therefore equates to O(n + n * log(n)). If you start with a max heap rather than an unsorted array, there will be no difference in the runtime because the build_max_heap() function will still take O(n) time to complete. However, the mere fact you are starting with a max heap means you must have built that heap prior to calling the heap sort algorithm, so you've actually increased the overall runtime by an extra O(n), thus taking O(2n * log(n)) in total.
because of the gravity of the earth
By understanding the time and space complexities of sorting algorithms, you will better understand how a particular algorithm will scale with increased data to sort. * Bubble sort is O(N2). The number of Ops should come out <= 512 * 512 = 262144 * Quicksort is O(2N log N) on the average but can degenerate to (N2)/2 in the worst case (try the ordered data set on quicksort). Quicksort is recursive and needs a lot of stack space. * Shell sort (named for Mr. Shell) is less than O(N4/3) for this implementation. Shell sort is iterative and doesn't require much extra memory. * Merge sort is O( N log N) for all data sets, so while it is slower than the best case for quicksort, it doesn't have degenerate cases. It needs additional storage equal to the size of the input array and it is recursive so it needs stack space. * Heap sort is guaranteed to be O(N log N), doesn't degenerate like quicksort and doesn't use extra memory like mergesort, but its implementation has more operations so on average its not as good as quicksort.
Merge sort and heap sort are both comparison-based sorting algorithms, but they differ in their approach to sorting. Merge sort divides the array into two halves, sorts each half separately, and then merges them back together in sorted order. It has a time complexity of O(n log n) in all cases and a space complexity of O(n) due to the need for additional space to store the merged arrays. Heap sort, on the other hand, uses a binary heap data structure to sort the array in place. It has a time complexity of O(n log n) in all cases and a space complexity of O(1) since it does not require additional space for merging arrays. In terms of efficiency, both merge sort and heap sort have the same time complexity, but heap sort is more space-efficient as it does not require additional space for merging arrays.
Bubble sort is an "in place" algorithm. Other than a temporary "switch" variable, no extra space is required.
selection sort
The worst case scenario for the Heap Sort algorithm is O(n log n) time complexity, which means it can be slower than other sorting algorithms like Quick Sort or Merge Sort in certain situations. This is because Heap Sort requires more comparisons and swaps to rearrange the elements in the heap structure.
You would sort the given elements of an array by a bubble sort or heap sort code!!
The running time of the heap sort algorithm is O(n log n), where n is the number of elements in the input array.
The runtime complexity of the heap sort algorithm is O(n log n), where n is the number of elements in the input array.
The time complexity of the heap sort algorithm is O(n log n), where n is the number of elements in the input array.
+ reasonable fast in worst and average cases, n lg n + O(n) + in place - best case still n lg n
Heap sort and merge sort are both comparison-based sorting algorithms. The main difference between them is in their approach to sorting. Heap sort uses a binary heap data structure to sort elements. It repeatedly extracts the maximum element from the heap and places it at the end of the sorted array. This process continues until all elements are sorted. Merge sort, on the other hand, divides the array into two halves, sorts each half recursively, and then merges the sorted halves back together. In terms of time complexity, both heap sort and merge sort have a time complexity of O(n log n) in the worst-case scenario. However, in practice, merge sort is often considered more efficient because it has a more consistent performance across different input data sets. Heap sort can have a higher constant factor in its time complexity due to the overhead of maintaining the heap structure.
The best case scenario for the performance of the heap sort algorithm is when the input data is already in a perfect heap structure, resulting in a time complexity of O(n log n).
The running time of the heap sort algorithm is O(n log n) in terms of time complexity.