How do you sort an array in Logn time?
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How do you sort an array in Logn time?
It essentially follows two steps:
- Divide the unsorted list into sub-lists until there are N sub-lists with one element in each ( N is the number of elements in the unsorted list).
- Merge the sub-lists two at a time to produce a sorted sub-list; repeat this until all the elements are included in a single list.
Can you sort in log n?
This means that any sorting algorithm has a lower bound of n , and since n > log(n) , a log(n) sort is impossible.
Can you sort in n time?
When k = O(n), the sort runs in O(n) time. The basic idea of counting sort is to determine, for each input element x, the number of elements less than x. This information can be used to place element x directly into its position in the output array.
Is it possible to sort an array faster than O N time?
A radix sort can beat O(n log n), and it works for a fairly wide variety of data (but definitely not everything). If your idea of practical and generic limits the algorithm to one that directly compares elements, then no — nothing does (or ever can) be better than O(n log n). That’s been proven for quite some time.
Can you sort array in O N?
Yes. If you do not have any limitations regarding space complexity then you can sort the array with o(n) time complexity. Suppose you have an array of k (let k =10) elements.
Which sort algorithm is log n?
MergeSort and HeapSort algorithms have O(nlogn) time in Best, Average & Worst case, while Quick sort has O(nlogn) time complexity only in Best and Average Case.
Can you sort faster than n log n?
It turns out that this is not possible when we restrict ourselves to sorting algorithms that are based on comparing array elements. The lower bound for the time complexity can be proved by considering sorting as a process where each comparison of two elements of the array gives more information about the its contents.
Is it possible to sort N elements in fewer than n steps?
No, you cannot have a sorting algorithm that runs in less than O(n) time. Suppose you want to sort a list of n numbers. You must always look at all n numbers, and this process itself runs in O(n) time.