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In computer science, an algorithm is said to be asymptotically optimal if, roughly speaking, for large inputs it performs at worst a constant factor (independent of the input size) worse than any possible algorithm. It is a term commonly encountered in computer science research as a result of widespread use of big O notation.
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optimal asymptotically algorithm algorithms input inputs use example data may time problem computer constant possible require comparisons range practice better
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| better performance on specific inputs | instance of | New algorithms may also present advantages | 0.80 | text |
| decreased use of other resources | instance of | New algorithms may also present advantages | 0.80 | text |
| or being simpler to describe | instance of | New algorithms may also present advantages | 0.80 | text |
| implement | instance of | New algorithms may also present advantages | 0.80 | text |
| memory cache | instance of | hardware optimizations | 0.80 | text |
| parallel processing may be | instance of | hardware optimizations | 0.80 | text |
| Asymptotically optimal algorithm | related to Speedup | The | 0.60 | section |
| Asymptotically optimal algorithm | related to Speedup | Blum's | 0.60 | section |
| Asymptotically optimal algorithm | related to Speedup | However | 0.60 | section |
| Asymptotically optimal algorithm | related to Speedup | For | 0.60 | section |
| Asymptotically optimal algorithm | related to Speedup | Ackermann | 0.60 | section |
| Asymptotically optimal algorithm | related to Speedup | Omega | 0.60 | section |
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