Algorithm Analysis

Algorithm analysis is one of the important sub-disciplines of computational complexity theory that aims at assessing the amount of resources required to perform a particular operation, in terms of time and space. To that end, this analysis wants to capture knowledge about an algorithm and its expected computing behavior prior to running it on a computer.

This approach is also used in case of comparisons between various algorithms, to evaluate how each of them solves a given problem so as to identify its effectiveness. They assist the developers in drawing benchmarks and making sound decisions on the usage of the right algorithms that suit an application.

Algorithm analysis not only explains how well an algorithm works in terms of time and space complexity but also enables to estimate how well it will work when the size of the problem is extended. This is especially the case in the present world where the amount of data that needs to be analyzed is huge. This is useful in predicting the performance of an algorithm in the future with large input to allow the developer to make changes on the algorithm at the early stage of the design to prevent the software from slowing down as the number of input increases. This is especially the case since there are times when an algorithm that may be efficient for small data sets can be a horror or even practically unfeasible when the data sets are large.

However, algorithm analysis becomes very important especially in a system that has a limited capacity of computation or storage. The developers can choose algorithms that not only fulfil the performance requirement but also the constraints of the system and guarantee the proper running of the software in the environment. This is most helpful in the case of the embedded systems and mobile applications and other situations in which the resources are scarce and need to be protected. By means of the analysis, it is possible to achieve the best balance between computational complexity and practical applicability in the development of software solutions.