Tech moves fast! Stay ahead of the curve with Techopedia!
Join nearly 200,000 subscribers who receive actionable tech insights from Techopedia.
Space complexity in algorithm development is a metric for how much storage space the algorithm needs in relation to its inputs. This measurement is extremely useful in some kinds of programming evaluations as engineers, coders and other scientists look at how a particular algorithm works.
Professionals may use concrete equations to show an algorithm’s space complexity where N equals inputs. Different forms of presentation such as “big O” notation may be used.
One reason that it is important to estimate the space complexity of an algorithm, the space it needs relative to inputs, is that some algorithms are designed with particular limitations. Some are designed with a cap on total storage space use, which can result in rough or imprecise results. Others are made to enforce precise results regardless of the space used.
Space complexity is a straightforward way for professionals to look at the footprint of an algorithm. For instance, engineers may add up memory to store program instructions, memory for variable values, and other types of central or auxiliary memory to get a total number of all of the memory that the program uses with a given number of inputs. This also helps professionals to plan for resource needs in operation.