Algorithm Complexity

An understanding of an algorithm&s;s complexity is an important planning consideration, especially before processing huge data sets, as the run-time required to test algorithms may be costly. Before code is compiled and run, algorithmic efficiency can be estimated by relatively simple means. Algorithm complexity analysis is rooted in the counting of operations and concerns how well an algorithm scales with data. It is distinct from algorithm performance, which in addition to the complexity of the algorithm, depends on the unique aspects of a computer, such as its CPU, memory, disk read/write speed, bus bandwidth, and the latency of data distribution over networks. In addition to ignoring the parameters unique to specific computing environments, complexity analysis ignores the effects of constants that are hard-coded in algorithms, focusing instead on how well an algorithm generally processes a series of input data. The limits of such performance is a matter of asymptotic analysis .