Hash Tables + probability question

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The discussion revolves around a homework question related to hash tables and the application of Markov's inequality to analyze the expected length of probes. The user has successfully solved earlier parts of the problem but struggles with proving that the expected length of probes is O(log n). They attempt to manipulate Markov's inequality but find it challenging to derive a useful upper bound. The conversation highlights the difficulty in estimating the variable t, which represents the number of probes, and its implications on the expected value E(X). Ultimately, the user seeks insights to resolve their confusion and validate their approach to the problem.
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Now before I'll post the question , I want to be clear. Technically , this WAS a homework question. BUT since I don't have to submit it (because I finished with the homework submitting process , and now studying for the test), I think it is legitimate to post here the homework file + I am doing it because I couldn't really solve it. (and there is no official solutions because our lecturers and his assistance are pretty god damn lazy:( )

now this is question 6 (with the hash table of size 2n to store n elements).
I've solved the first three sub-questions:

the first one is upper bounded by p=1/2 and it distributes as geometric random variable.
the second one is just substituting in the first sub question.
the third one is basically the sum of all n Xi insertions with the answer of sub question b.

Now, the forth questions deals somehow with Markov's inequality and about to prove that the expected length of probes is O(logn).

So I know that Markov's inequality means :
P(x>=t)<= E(X)/t

and although i tried to used this fact to get an upper bound , I couldn't find any solution or a lead.

Note: This is a new question which didn't appear on previous years assignments. If you have a reason to believe this question was made inappropriately or maybe even some mistakes.

Looking forward for your help
and thank you,
Aviv
 

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Rewrite P(x>t) < E(X)/t as t*P(x>t) < E(X), or E(X) = t*P(x>t) + v for some v > 0. Show that t*P(x>=t) + v is O(Log n).
 
It was the first thing i have done, obviously. yet couldn't get any results.

according to the questions P(X>=t)<= n/2^t (according to question 6.1 the probability to probe more then k times is 2^-k , sum it n times and you will get n/2^t).

So that leads us to nt/2^t<=E(X).

after doing log on both sides (log2) i get logn+logt-t<=log (E(X)). since t can be any number, I can't block it or take it out. I lack insights about that situation. But even if I do manage to get E(X)>={log expression}. All I get is a lower bound. then I will have to show an upper bound.

:(
Thanks in advance
 
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If t \approx 1, then log t \approx t - 1.
 
I don't think t has any estimations, why would t\approx1 . t is the number of probes
but even if it had anything like it i would get that E(X)=\Omega(n), which is the complete contrary.
 
Are you saying that what you have does not work for t = 1?
 
Yes, I think at least. t is the number of probes on the array. and the question says "use markov's inequality to prove that the longest probe sequence is E(X)=O(logn)".

there is no reason to believe that t = 1 (it should even be much bigger to my opinion), and even if it were true, i would still get the wrong answer.
 
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