What Does o(1) Mean in the Longest Increasing Subsequence Problem?

AI Thread Summary
The discussion revolves around the Longest Increasing Subsequence (LIS) problem, specifically the expectation of the length of the LIS for i.i.d. random variables uniformly distributed on [0,1]. The user seeks to understand the notation "o(1)" in the context of proving that E[X] is bounded below by a specific expression involving √n. They mention using Erdős' lemma, which provides a weaker bound of E[X] ≥ (1/2)√n. The conversation emphasizes the need for a stronger lower bound and clarification on the implications of "o(1)" in this mathematical context. Overall, the thread highlights the complexities of deriving precise bounds for the expected length of the longest increasing subsequence.
Naumberg
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Problem:

"Let x_1, ..., x_n be i.i.d random variables uniformly on [0,1]. Let X be the length of the longest increasing subsequence of x_1, ..., x_n. Show that E[X] \ge (1-o(1))(1-e^{-1}) \sqrt{n}."


Hi forum!

Using the Erdos' lemma I can only deduce that E[X] \ge \frac{1}{2} \sqrt{n}, which is a weaker bound unfortunately.

I would appreciate any further ideas!

Thanks for your help,
Michael
 
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What does the notation "o(1)" mean in this context?
 
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