Computational complexity is a measure of the number of operations that a given algorithm requires to complete its task. It takes into account both time and space complexity, meaning the amount of time it takes for the algorithm to run, as well as the amount of memory required to execute the algorithm. 

Goal of Computational Complexity

The goal of studying computational complexity is to determine which algorithms are most efficient for a given task. When discussing computational complexity, we often refer to Big O notation, which gives us an upper bound on the time and/or space complexity of an algorithm. 

Big O notation allows us to compare different algorithms and determine which one will be more efficient in terms of time and space usage. We can also use this notation to determine whether an algorithm is feasible for a given problem size; if its Big O complexity is too high, then it may not be suitable for solving the problem. 

Worst-Case Complexity

The worst-case and average-case complexities are two important metrics used while analyzing algorithms. The worst-case complexity describes how long it would take an algorithm to solve a problem if all inputs were provided in their least favorable order. 

Average-Case Complexity

On the other hand, average-case complexity tells us how long it would take an algorithm to solve a problem if inputs were provided in random order. Knowing these two complexities helps us understand how resilient an algorithm is against adverse input data, or how well it performs when faced with inputs that don’t fit certain patterns. In addition to Big O notation, there are several other metrics used in computational complexity analysis, such as amortized analysis and competitive analysis techniques. 

Amortized analysis measures how much time or memory resources are needed over several operations instead of just one operation; this metric can be useful when studying data structures such as stacks or queues. 

Competitive Analysis Techniques

Competitive analysis techniques compare different algorithms using games like chess or checkers; these can be used to determine which approach will perform better under certain conditions or scenarios. 

Advantages and Disadvantages

The advantage of considering computational complexity is that it allows for the evaluation and comparison of different algorithms in terms of their efficiency, helping developers to choose the best option for a specific problem under given constraints. 

There are different measures of computational complexity, such as time complexity, which refers to the number of steps or instructions required by an algorithm to solve a problem; and space complexity, which refers to the amount of memory needed for an algorithm to operate. 

Other complexity measures can consider the number of arithmetical operations, the number of comparisons or any other type of resource used by an algorithm. The disadvantage of considering computational complexity is that it is often impractical to calculate precisely, as it may depend on factors such as the size of the input data or the hardware used. 

Moreover, some problems may be technically solvable despite having a high computational complexity, while others may be theoretically unsolvable even with a low computational complexity. 

Therefore, developers and researchers often use computational complexity as a rough estimate of the performance of different algorithms, but it should not be the sole criterion for choosing or evaluating them. Other factors such as maintainability, scalability, or user-friendliness, should also be taken into account in software development.

Conclusion

Overall, computational complexity is an important concept when designing algorithms for any type of application; understanding its various metrics can help you make informed decisions about which approach will produce the most optimal results for your particular use case.