Worst Case, Average Case, and Best Case

The Concept of Worst Case, Average Case, and Best Case in Algorithm Analysis.

In this article, I will introduce you to the concept of worst case, average case and best case analysis of the algorithm.

Introduction to Worst Case, Average Case and Best Case

In computing, the worst, average, and best case of an algorithm depends on the size of the user input value. To understand these terms, let’s go through them one by one.

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Worst Case Analysis:

In the worst-case analysis, we calculate the upper limit of the execution time of an algorithm. It is necessary to know the case which causes the execution of the maximum number of operations.

For linear search, the worst case occurs when the element to search for is not present in the array. When x is not present, the search () function compares it with all the elements of arr [] one by one. Therefore, the temporal complexity of the worst case of linear search would be Θ (n).

Average Case Analysis:

In the average case analysis, we take all possible inputs and calculate the computation time for all inputs. Add up all the calculated values ​​and divide the sum by the total number of entries.

We need to predict the distribution of cases. For the linear search problem, assume that all cases are uniformly distributed. So we add all the cases and divide the sum by (n + 1).

Best Case Analysis:

In the best case analysis, we calculate the lower bound of the execution time of an algorithm. It is necessary to know the case which causes the execution of the minimum number of operations. In the linear search problem, the best case occurs when x is present at the first location.

The number of operations in the best case is constant. The best-case time complexity would therefore be Θ (1) Most of the time, we perform worst-case analysis to analyze algorithms. In the worst analysis, we guarantee an upper bound on the execution time of an algorithm which is good information.

The average case analysis is not easy to do in most practical cases and is rarely done. In the average case analysis, we need to predict the mathematical distribution of all possible inputs. The Best Case analysis is wrong. Guaranteeing a lower bound on an algorithm does not provide any information because in the Worst Case scenario an algorithm can take years to run.

Conclusion:

For some algorithms, all cases are asymptotically the same, that is, there is no worst and best case. For example, Sort by merge. Merge sorting performs Θ (nLogn) operations in all cases. Most of the other sorting algorithms present the worst and best cases.

For example, in the typical quicksort implementation, the worst occurs when the input array is already sorted and the best occurs when the pivot elements always divide the table into two halves.

For insert sorting, the worst case occurs when the array is sorted in reverse order and the best case occurs when the array is sorted in the same order as the output.

Hope you liked this article on the concept of worst case, middle case and best case analysis of algorithms. Please feel free to ask your valuable questions in the comments section below.

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Aman Kharwal

I am a programmer from India, and I am here to guide you with Data Science, Machine Learning, Python, and C++ for free. I hope you will learn a lot in your journey towards Coding, Machine Learning and Artificial Intelligence with me.

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