Definitions, mental models, and quick examples for the ideas you’ll see in the visualizer.
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Describes how an algorithm’s runtime or memory grows with input size.
A measure of how runtime scales with input size.
How much extra memory an algorithm needs as n grows.
A function that calls itself to solve smaller subproblems.
Caching results of expensive calls to avoid recomputation.
Split a problem into smaller pieces, solve them, then combine.
A sort that preserves the relative order of equal keys.
Key/value store with average O(1) operations.
Average cost per operation over a sequence, even if some are expensive.