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Why is big omega of finding a peak in an unsorted array Omega(lg n), not Omega(1)?

I understand that peak finding is O(lg n), because in the worst case, we find a peak in the last possible step, so when drawing a decision tree, the height of the tree (lg n) would be the worst case situation. However, in the best case, can't we get "lucky" and find a peak in the first try?

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  • $\begingroup$ @ gridproquo It depends upon the algorithm. Please write the pseudocode of the peak finding algorithm you are using. $\endgroup$ – aaag Mar 13 '17 at 5:43
  • $\begingroup$ What is peak finding? $\endgroup$ – Yuval Filmus Mar 13 '17 at 6:43
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I think you got a bit confused with the definition of Big-Omega ($\Omega$). The $\Omega(f(n))$ is a lower bound for an algorithm. It means that your algorithm's running time can not be less than $kf(n)$ where $k$ is a constant. And it applies to worst case not the best case.

edit If you calculate your algorithm running time as $c\log{n}$, it's asymptotic analysis says that it is $\Omega(\log{n})$.

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big omega notation

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  • $\begingroup$ Isn't the worst case big O? $\endgroup$ – gridproquo Mar 13 '17 at 2:33
  • $\begingroup$ These "Big" stuff are the boundaries. Big-O is an upper bound boundary and Big-Omega is a lower bound boundary. $\endgroup$ – Iraj Hedayati Mar 13 '17 at 2:38
  • $\begingroup$ Ah, so I can think of big omega as the worst of the best cases? $\endgroup$ – gridproquo Mar 13 '17 at 2:39
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    $\begingroup$ You should always forget about the best case in algorithm analysis. It is always worst case. So, Big-Omega is the best case of worst cases. P.S. sometime in very rare cases, we also consider average case $\endgroup$ – Iraj Hedayati Mar 13 '17 at 2:43

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