Question

1. # In Binary Search, Which Will Be The Scenario For Best Case Performance?

In computer science, binary search is a search algorithm for sorted data structures in which the subject is searched by splitting it into two parts and comparing each part to the target. In this blog post, we will explore the tradeoffs involved in using binary search as the scenario for best case performance. By understanding these tradeoffs, you can make smarter decisions when choosing an algorithm for your application.

## Binary Search Algorithm

Binary search algorithm is a divide and conquer algorithm that is used to find the best item from a sorted list. The algorithm starts by determining if the first item in the list is greater than or equal to the second item. If it is, then the algorithm moves on to the next item and repeats the process. If however, the first item is not greater than or equal to the second item, then binary search will start by comparing the second item with the third item. If they are equal, then binary search will go to the fourth item and continue this process until either one of the items in the list is found or there are no more items in the list.

## Best Case Scenario

In binary search, the best case scenario is when the sorted list contained the desired item at the very top. In this scenario, the algorithm will visit each element of the list in turn until it finds the desired item. If the desired item is not found on the list, then it will continue to look for the item until it is found. The worst case scenario is when the sorted list does not contain the desired item, in which case the algorithm will need to search through all of its elements.

## Worst Case Scenario

In binary search, the worst case scenario is when the searched item is not found. In this scenario, the algorithm will perform two cycles of the search (or two comparisons). The best case scenario is when the searched item is found in the first cycle. In this scenario, the algorithm will only perform one comparison and therefore will be faster than if it performed two cycles.

The trade-off between these two scenarios is that in the best case scenario, there is a 1 in 2 chance of finding the searched item while in the worse case scenario there is a 1 in n chance of finding the searched item. This means that on average, using binary search will result in more comparisons being made than using a different sorting algorithm but with less risk of failing to find the sought after item.

## Conclusion

In this article, we have investigated the performance of two different algorithms in a binary search scenario – the bubble sort and the selection sort. We found that while both algorithms perform similarly in most cases, the bubble sort is faster when there are relatively few elements to be sorted. The selection sort is faster when there are many elements to be sorted, as it can avoid sorting through intermediate results multiple times. This difference in speed may depend on your specific application and is something you should take into account when choosing an algorithm for your data.