DSA
Big-O
upper bound on growth (drop constants & lower terms)
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Complexity and Big-ODSAMore terms from this board
- amortizedcost averaged over a sequence of ops
- best/average/worst casethe input that gives least/typical/most cost
- dominant termthe fastest-growing part (all that survives Big-O)
- in-placeuses O(1) extra space
- input-sensitivecost depends on which input, not just its size
- linearithmicn log n
- Master theoremrecurrence → complexity
- memoizationcache results to trade space for time
- the cache caveatBig-O ignores the constant, and the constant is often memory locality
- time vs space complexityops vs memory as a function of n
- Θ (theta)tight bound (matches above & below)
- Ω (omega)lower bound