Table of Contents

## What is best average and worst case analysis of algorithm?

Best case is the function which performs the minimum number of steps on input data of n elements. Worst case is the function which performs the maximum number of steps on input data of size n. Average case is the function which performs an average number of steps on input data of n elements.

**What are the worst case and average case complexities of a binary search?**

Binary search’s average and worst case time complexity is O ( log n ) O(\log n) O(logn), while binary search tree does have an average case of O ( log n ) O(\log n) O(logn), it has a worst case of O ( n ) O(n) O(n).

### What is the best case for binary search?

O(1)

Binary search algorithm/Best complexity

The best case of binary search is when the first comparison/guess is correct(the key item is equal to the mid of array). It means, regardless of the size of the list/array, we’ll always get the result in constant time. So the best case complexity is O(1).

**What is the average case of binary search?**

The average cost of a successful search is about the same as the worst case where an item is not found in the array, both being roughly equal to logN. So, the average and the worst case cost of binary search, in big-O notation, is O(logN).

## What is average case in algorithm?

In computational complexity theory, the average-case complexity of an algorithm is the amount of some computational resource (typically time) used by the algorithm, averaged over all possible inputs.

**What will be the worst case in binary search?**

The time complexity of the binary search algorithm is O(log n). The best-case time complexity would be O(1) when the central index would directly match the desired value. The worst-case scenario could be the values at either extremity of the list or values not in the list.

### What is the best and worst case of binary search?

For a binary search, the best-case occurs when the target is at the end of the search list. For a binary search, the worst-case is when the target item is not in the search list. For a binary search, the worst-case is when the target is found in the middle of the search list.

**How to calculate the best case of an algorithm?**

For the linear search problem, let us assume that all cases are uniformly distributed (including the case of x not being present in array). So we sum all the cases and divide the sum by (n+1). Following is the value of average case time complexity. In the best case analysis, we calculate lower bound on running time of an algorithm.

## Which is the worst case complexity of linear search?

When x is not present, the search () functions compares it with all the elements of arr [] one by one. Therefore, the worst case time complexity of linear search would be Θ (n). In average case analysis, we take all possible inputs and calculate computing time for all of the inputs.

**When do you do a worst case analysis?**

Most of the times, we do worst case analysis to analyze algorithms. In the worst analysis, we guarantee an upper bound on the running time of an algorithm which is good information. The average case analysis is not easy to do in most of the practical cases and it is rarely done.

### Which is an example of a binary search algorithm?

There is a linear array ‘a’ of size ‘n’. Binary search algorithm is being used to search an element ‘item’ in this linear array. If search ends in success, it sets loc to the index of the element otherwise it sets loc to -1.