Computational complexity with an epsilon

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SUMMARY

The discussion focuses on the interpretation of computational complexity expressions that include an epsilon (ε), specifically O(n^{2+ε}) for every ε > 0. This notation indicates that the complexity is strictly greater than O(n²) but less than O(n^{2.1}). Participants clarify that complexities like O(n³) and O(n⁴) also fall under the broader category of O(n³), reinforcing the hierarchical nature of Big O notation.

PREREQUISITES
  • Understanding of Big O notation
  • Familiarity with computational complexity theory
  • Basic knowledge of asymptotic analysis
  • Experience with algorithm performance evaluation
NEXT STEPS
  • Research the implications of O(n^{k}) complexities for various values of k
  • Explore the concept of epsilon in algorithm analysis
  • Learn about the differences between polynomial and exponential time complexities
  • Study examples of algorithms that exhibit O(n^{2+ε}) behavior
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Computer scientists, algorithm designers, and students studying computational complexity who seek to deepen their understanding of Big O notation and its implications in algorithm performance.

yavanna
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What does that mean when there's an [itex]\epsilon[/itex] in the complexity, such as
[itex]O(n^{2+\epsilon})[/itex] for every [itex]\epsilon >0[/itex]
 
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yavanna said:
What does that mean when there's an [itex]\epsilon[/itex] in the complexity, such as
[itex]O(n^{2+\epsilon})[/itex] for every [itex]\epsilon >0[/itex]

Welcome to PF, yavanna! :smile:

I would tend to take it very literally.
It says the complexity is greater than [itex]O(n^{2})[/itex].
But it is less than for instance [itex]O(n^{2.1})[/itex].

Note that a complexity of [itex]O(n^{3})[/itex] as well as [itex]O(n^{4})[/itex] implies a complexity of [itex]O(n^{3})[/itex].
 
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