Linked from

The 19 pages that link to Time complexity, each with the reason it gives.

• Dynamic programmingRelated: The number of distinct states and transitions usually determines dynamic-programming runtime.

• Breadth-first searchRelated: With adjacency lists, breadth-first search runs in time proportional to vertices plus edges.

• Depth-first searchRelated: With adjacency lists, DFS runs in time proportional to vertices plus edges.

• BacktrackingRelated: The number of branches can grow exponentially, making search cost a central concern.

• Data structureRelated: Operation costs determine whether a structure remains efficient as data grows.

• Edmonds–Karp algorithmRelated: Edmonds–Karp runs in O(VE²), where V and E count vertices and edges.

• Dynamic arrayRelated: Dynamic arrays offer constant-time indexing and amortized constant-time appends, but linear-time middle insertions.

• Nondeterministic Turing machineRelated: For a nondeterministic machine, time bounds apply to every computation path.

• Floyd–Warshall algorithmRelated: Three nested vertex loops give Floyd–Warshall cubic running time.

• Linked listRelated: It captures the tradeoff between constant-time local edits and linear-time position access.

• Associative arrayRelated: Lookup and update costs depend on the associative array's implementation and input.

• Blum's speedup theoremRelated: Running time is a familiar example of a Blum complexity measure.

• Theory of computationRelated: It is a primary way to compare the efficiency of computational solutions.