Computational complexity refers to the amount of time, space, and resources required to solve a given problem or perform a specific task. In other words, it is a measure of how difficult it is to solve a problem or perform a task using a computer.

One example of computational complexity is sorting algorithms. Different sorting algorithms have varying levels of computational complexity. For instance, the bubble sort algorithm has a time complexity of O(n^2), meaning that it takes longer to sort a large number of items compared to other sorting algorithms with a lower time complexity, such as the quicksort algorithm, which has a time complexity of O(nlogn).

Another example of computational complexity is the traveling salesmanperson problem. This problem involves finding the shortest possible route that a salesperson can take to visit a given set of cities and return to the starting point. The time complexity of this problem is O(n!), meaning that it becomes increasingly difficult to solve as the number of cities increases.

Overall, computational complexity is an important concept in computer science, as it helps to determine the feasibility and efficiency of algorithms and other computational tasks. By understanding the computational complexity of different algorithms and problems, researchers and developers can select the most appropriate methods for solving specific problems and tasks, and can also identify ways to improve the efficiency of existing algorithms.