Linked from

The 14 pages that link to Amortized analysis, each with the reason it gives.

• Big O notationRelated: Amortized bounds use asymptotic notation to describe costs over operation sequences.

• Greedy algorithmRelated: It can analyze the total cost of greedy procedures whose individual steps have varying expense.

• Time complexityRelated: It captures sequences where occasional costly operations are offset by many cheap ones.

• Space complexityRelated: It provides a way to describe storage behavior across operation sequences.

• Data structureRelated: Dynamic arrays and similar structures can have occasional expensive updates but low amortized cost.

• Analysis of algorithmsRelated: It handles operations whose occasional high costs are offset by many cheap ones.

• Hash tableRelated: It explains why occasional expensive resizing can coexist with cheap typical insertions.

• Divide-and-conquer algorithmRelated: It can analyze recursive algorithms whose costs vary across calls or stages.

• Edmonds–Karp algorithmRelated: The runtime proof tracks how often residual edges can become critical across augmentations.

• Priority queueRelated: It distinguishes per-operation costs from long-run costs in priority-queue implementations.

• Dynamic arrayRelated: It captures why repeated appends are cheap on average despite occasional full-array copies.

• Linked listRelated: It helps compare linked lists with resizable arrays whose occasional growth is expensive.

• Red–black treeRelated: It distinguishes update costs across sequences from the tree’s worst-case logarithmic bounds.

• Set (abstract data type)Related: Dynamic resizing can make individual insertions costly while keeping their amortized cost low.