Linked from

The 15 pages that link to Space complexity, each with the reason it gives.

• Big O notationRelated: Big O describes memory growth as well as running-time growth.

• Dynamic programmingRelated: Stored subproblem values can dominate memory, though some tables admit compression.

• Data structureRelated: Memory overhead can make a fast structure impractical under limited storage.

• Analysis of algorithmsRelated: It measures a resource distinct from execution time, including auxiliary storage.

• Dynamic arrayRelated: Unused capacity consumes memory in exchange for fewer reallocations.

• Floyd–Warshall algorithmRelated: Its distance table requires quadratic space in the number of vertices.

• HeapsortRelated: Its in-place implementation uses constant auxiliary space, a practical advantage over merge sort.

• Linked listRelated: Links and per-node allocation add memory overhead beyond stored values.

• Theory of computationRelated: It tracks a resource distinct from running time in complexity classifications.