Found 546 Articles for Algorithms

This is an example of the non-comparison sorting technique. It is used where the number of items and the range of possible key values is approximately the same.To perform this sort, we need to make some holes. The number of holes needed is decided by the range of numbers. In each hole, items are inserted. Finally deleted from the hole and stored into an array for sorted order.The complexity of Pigeon-Hole Sort TechniqueTime Complexity: O(n+2^k)Space Complexity: O(2^k)Input and OutputInput: The unsorted list: 802 630 20 745 52 300 612 932 78 187 Output: Data before Sorting: 802 630 20 745 ... Read More

The merge sort technique is based on divide and conquers technique. We divide the whole dataset into smaller parts and merge them into a larger piece in sorted order. It is also very effective for worst cases because this algorithm has lower time complexity for the worst case also.The complexity of Merge Sort TechniqueTime Complexity: O(n log n) for all casesSpace Complexity: O(n)Input and OutputInput: The unsorted list: 14 20 78 98 20 45 Output: Array before Sorting: 14 20 78 98 20 45 Array after Sorting: 14 20 20 45 78 98Algorithmmerge(array, left, middle, right)Input − The data set array, left, middle ... Read More

This sorting technique is similar with the card sorting technique, in other words, we sort cards using insertion sort mechanism. For this technique, we pick up one element from the data set and shift the data elements to make a place to insert back the picked up an element into the data set.The complexity of the Insertion Sort TechniqueTime Complexity: O(n) for best case, O(n^2) for average and worst caseSpace Complexity: O(1)Input and OutputInput: The unsorted list: 9 45 23 71 80 55 Output: Array before Sorting: 9 45 23 71 80 55 Array after Sorting: 9 23 45 55 ... Read More

Heap sort is performed on the heap data structure. We know that heap is a complete binary tree. Heap tree can be of two types. Min-heap or max heap. For min heap the root element is minimum and for max heap the root is maximum. After forming a heap, we can delete an element from the root and send the last element to the root. After these swapping procedure, we need to re-heap the whole array. By deleting elements from root we can sort the whole array.The complexity of Heap Sort TechniqueTime Complexity: O(n log n)Space Complexity: O(1)Input and OutputInput: A ... Read More

Cycle Sort is an in-place sorting algorithm. It is also a comparison based sort and efficient for any other in-place sorting technique. It finds the minimum number of memory write to perform the sorting tasks.The complexity of Cycle Sort TechniqueTime Complexity: O(n^2)Space Complexity: O(1)Input and OutputInput: A list of unsorted data: 23 63 98 74 20 14 36 45 99 78 Output: Array before Sorting: 23 63 98 74 20 14 36 45 99 78 Array after Sorting: 14 20 23 36 45 63 74 78 98 99AlgorithmcycleSort(array, size)Input − An array of data, and the total number in the ... Read More

Counting sort is a stable sorting technique, which is used to sort objects according to the keys that are small numbers. It counts the number of keys whose key values are same. This sorting technique is effective when the difference between different keys are not so big, otherwise, it can increase the space complexity.The complexity of counting Sort TechniqueTime Complexity: O(n+r)Space Complexity: O(n+r)Input and OutputInput: A list of unsorted data: 2 5 6 2 3 10 3 6 7 8 Output: Array before Sorting: 2 5 6 2 3 10 3 6 7 8 Array after Sorting: 2 2 3 ... Read More

The basic idea of comb sort and the bubble sort is same. In other words, comb sort is an improvement on the bubble sort. In the bubble sorting technique, the items are compared with the next item in each phase. But for the comb sort, the items are sorted in a specific gap. After completing each phase, the gap is decreased. The decreasing factor or the shrink factor for this sort is 1.3. It means that after completing each phase the gap is divided by 1.3.The complexity of Comb Sort TechniqueTime Complexity: O(n log n) for the best case. O(n^2/2^p) (p ... Read More

In the Bucket Sorting technique, the data items are distributed in a set of buckets. Each bucket can hold a similar type of data. After distributing, each bucket is sorted using another sorting algorithm. After that, all elements are gathered on the main list to get the sorted form.The complexity of the Bucket Sort TechniqueTime Complexity: O(n + k) for best case and average case and O(n^2) for the worst case.Space Complexity: O(nk) for worst caseInput and OutputInput: A list of unsorted data: 0.25 0.36 0.58 0.41 0.29 0.22 0.45 0.79 0.01 0.69 Array before Sorting: 0.25 0.36 0.58 0.41 ... Read More

Bubble Sort is a comparison based sorting algorithm. In this algorithm adjacent elements are compared and swapped to make the correct sequence. This algorithm is simpler than other algorithms, but it has some drawbacks also. This algorithm is not suitable for a large number of data set. It takes much time to solve the sorting tasks.The complexity of the Bubble Sort TechniqueTime Complexity: O(n) for best case, O(n^2) for average and worst caseSpace Complexity: O(1)Input and OutputInput: A list of unsorted data: 56 98 78 12 30 51 Output: Array after Sorting: 12 30 51 56 78 98AlgorithmbubbleSort( array, size)Input ... Read More

Like the binary search, it also separates the lists into sub-lists. This procedure divides the list into three parts using two intermediate mid values. As the lists are divided into more subdivisions, so it reduces the time to search a key value.The complexity of Ternary Search TechniqueTime Complexity: O(log3 n)Space Complexity: O(1)Input and OutputInput: A sorted list of data: 12 25 48 52 67 79 88 93 The search key 52 Output: Item found at location: 3AlgorithmternarySearch(array, start, end, key)Input − An sorted array, start and end location, and the search keyOutput − location of the key (if found), otherwise wrong ... Read More