## What is the time complexity of merge?

Time complexity of Merge Sort is O(n*Log n) in all the 3 cases (worst, average and best) as merge sort always divides the array in two halves and takes linear time to merge two halves.

**What is the time and space complexity of merge?**

nMerge sort / Space complexity

**What is the time complexity of a constant?**

If an algorithm’s time complexity is constant, it means that it will always run in the same amount of time, no matter the input size. For example, if we want to get the first item of an array, it doesn’t matter how big the input size is.

### Why merge sort complexity is O nLogn?

The complexity of merge function is O(n),as is it takes 2 arrays as an input,compare them and give output in new. As it is comparing each element with every other element in the array,the complexity of this merge function comes out to be O(n).

**What is the time complexity equation and time complexity of 3 way merge sort?**

The time complexity of 3 way merge sort is nlog3n.

**What is the best case time complexity of merge sort?**

O(n log n)

What will be the best case time complexity of merge sort? Explanation: The time complexity of merge sort is not affected in any case as its algorithm has to implement the same number of steps. So its time complexity remains to be O(n log n) even in the best case.

## What is the time complexity of quick sort?

To sort an array of n distinct elements, quicksort takes O(n log n) time in expectation, averaged over all n! permutations of n elements with equal probability.

**What is the time complexity of the following?**

Code | Time complexity |
---|---|

sum = 0 | O(1) |

for (i=1; I <= n; i*=2) | O(logn) because I is incremented exponentially and loop will run for less number of times than n. |

for(j=1; j<=n; j++) | O(n) because j is incremented linearly and loop will run for n number of times. |

sum++ | O(1) |

**Is Nlogn faster than N 2?**

That means n^2 grows faster, so n log(n) is smaller (better), when n is high enough. Show activity on this post. Big-O notation is a notation of asymptotic complexity. This means it calculates the complexity when N is arbitrarily large.

### What is complexity of DFS?

The time complexity of DFS if the entire tree is traversed is O(V) where V is the number of nodes.