Editing Space complexity (section)

{{Short description|Computer memory needed by an algorithm}}
The '''space complexity''' of an [[algorithm]] or a [[data structure]] is the amount of memory space required to solve an instance of the [[computational problem]] as a function of characteristics of the input. It is the memory required by an algorithm until it executes completely.<ref>{{citation|title=Optimal Reliability Modeling: Principles and Applications|first1=Way|last1=Kuo|first2=Ming J.|last2=Zuo|publisher=John Wiley & Sons|year=2003|isbn=9780471275459|url=https://books.google.com/books?id=vdZ4Bm-LnHMC&pg=PA62|page=62}}</ref> This includes the memory space used by its inputs, called '''input space''', and any other (auxiliary) memory it uses during execution, which is called '''auxiliary space'''.

Similar to [[time complexity]], space complexity is often expressed asymptotically in [[big O notation|big ''O'' notation]], such as <math>O(n),</math>
<math>O(n\log n),</math> <math>O(n^\alpha),</math> <math>O(2^n),</math> etc., where {{mvar|n}} is a characteristic of the input influencing space complexity.