Best, Worst, and Average Case is a concept in computer science designed to analyze the different scenarios that may occur when running an algorithm. It is important to note that the best, worst, and average cases refer to the complexity of the given algorithm, not the implementation of it. The best-case scenario is the fastest possible execution time of the algorithm, while the worst-case scenario is the longest possible execution time of the algorithm, and the average case is the expected execution time of the algorithm when average random inputs are used.
The best-case scenario is typically the most desirable of the three scenarios as it would provide the quickest output or result of the algorithm. This is achieved by interpreting certain inputs which will cause the algorithm to run the fastest. In other words, the best-case scenario occurs when the input happens to be in a sorted order already and the algorithm does not need to perform any work to organize or rearrange the input. It is also important to note that the other two scenarios, worst and average, occur much more often in practice.
The worst-case scenario is the least desirable of the three scenarios, and is usually the longest to execute since the input requires more sorting or rearranging by the algorithm. In a worst-case scenario, the input is usually in the most difficult arrangement for the algorithm to analyze, thus taking more time than necessary.
The average case scenario is a relatively common occurrence in practice and is an important factor in the development of efficient algorithms. Since most input data is random in nature, many algorithms are designed with this in mind. Thus, the average-case scenario is that which the algorithm is most often able to handle efficiently. By examining the average-case time complexity of a given algorithm, one can calculate the expected performance of the algorithm in an acceptable amount of time.
In conclusion, best, worst, and average cases provide us with an important tool for understanding the performance of algorithms. By analyzing the best, worst, and average cases of a given problem, developers and engineers can decide which algorithm will most efficiently solve their problems.