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In this question, for the element which is not the part of the list, what is the average number of comparisons? I thought that it should be n+1 but my friends suggests that it should be n+3 but I didn’t understand his solution. How should I approach in this kind of questions?
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Is there any fast way to find total divisors of a very large number supposedly 10^18.
I have tried a method which is of o(n^(1/3))
Forgive me asking direct question without providing any background or something else.
The fastest algorithm for that would be General number field sieve, probably fits your question.
On this question there is some discussion ongoing about how to efficiently find all divisors. GNFS is used for factorization, so only accounts prime numbers. You'll have to derive all divisors from that, if you need to. How to I find all divisors from prime factorization?
Further reading: A beginner's guide to the Number Field Sieve; Factoring Integers with the Number Field Sieve
My teacher made this argument and asked us what could be wrong with it.
for an array A of n distinct numbers. Since there are n! permutations of A,
we cannot check for each permutation whether it is sorted, in a total time which
is polynomial in n. Therefore, sorting A cannot be in P.
my friend thought that it just should be : therefore sorting a cannot be in NP.
Is that it or are we thinking to easily about it?
The problem with this argument is that it fails to adequately specify the exact problem.
Sorting can be linear-time (O(n)) in the number of elements to sort, if you're sorting a large list of integers drawn from a small pool (counting sort, radix sort).
Sorting can be linearithmic-time (O(nlogn)) in the number of elements to sort, if you're sorting a list of arbitrary things which are all totally ordered according to some ordering relation (e.g., less than or equal to on the integers).
Sorting based on a partial order (e.g. topological sorting) must be analyzed in yet another way.
We can imagine a problem like sorting whereby the sortedness of a list cannot be determined by comparing adjacent entries only. In the extreme case, sortedness (according to what we are considering to be sorting) might only be verifiable by checking the entire list. If our kind of sorting is designed so as to guarantee there is exactly one sorted permutation of any given list, the time complexity is factorial-time (O(n!)) and the problem is not in P.
That's the real problem with this argument. If your professor is assuming that "sorting" refers to sorting integers not in any particular small range, the problem with the argument then is that we do not need to consider all permutations in order to construct the sorted one. If I have a bag with 100 marbles and I ask you to remove three marbles, the time complexity is constant-time; it doesn't matter that there are n(n-1)(n-2)/6 = 161700, or O(n^3), ways in which you can accomplish this task.
The argument is a non-sequitur, the conclusion does not logically follow from the previous steps. Why doesn't it follow? Giving a satisfying answer to that question requires knowing why the person who wrote the argument thinks it is correct, and then addressing their misconception. In this case, the person who wrote the argument is your teacher, who doesn't think the argument is correct, so there is no misconception to address and hence no completely satisfying answer to the question.
That said, my explanation would be that the argument is wrong because it proposes a specific algorithm for sorting - namely, iterating through all n! permutations and choosing the one which is sorted - and then assumes that the complexity of the problem is the same as the complexity of that algorithm. This is a mistake because the complexity of a problem is defined as the lowest complexity out of all algorithms which solve it. The argument only considered one algorithm, and didn't show anything about other algorithms which solve the problem, so it cannot reach a conclusion about the complexity of the problem itself.
I'm working with a lot of name data where the following events are happening:
In one stream the data is submitted as "Sung" and in the other stream "Snug" my initial thought to this was to convert Sung and Snug to where each character equals a number then the sums would be the same, so even if they transverse a character, I'd be able to bucket these appropriately.
The other is where in one stream it comes in as "Lillly" as opposed to "Lilly" in the other stream. I'd like to figure out how to fuzzy match these such that I can identify them. I'm not sure if this is possible in Oracle.
I'm working with many millions of data points and trying to figure out how to write these classification buckets such that I can stop having so much noise in my primary task of finding where people are truly different people as opposed to a clerical error.
Any thoughts would be very appreciated.
A common measure for such distance is called Levenshtein distance (Wikipedia here). This measures the "edit" distance between two strings -- number of edit operations needed to convert one into the other.
That's the good news. More good news is that Oracle even has an implementation in the UTL_MATCH library.
The bad news is that it is really, really expensive on millions of data points. Unfortunately, I cannot help you there so much. One idea is to determine which names are "close enough" because they already share a certain minimum number of characters.
Another method is to convert the strings to what they sound like. That is called soundex. You may be able to use the two together -- assuming your names are predominantly English (the soundex algorithm was invented by the US Census Bureau, so it would work best on names in America).
I was doing the derivation for masters theorem using the tree method and I noticed something.
So we have:
$T(n)=a*T(n/b) + n^c$
From this: we notice, the last level of the tree will have $a^(log_b_n)$ splits, which equals $n^(log_b_a)$
Now, if $a=b$, I get n splits in the last level, which is I've seen used in quick sort and merge sort, and if a
Is there a practical example for greater than n splits?
Where we actually repeat operations for elements?
*Also, math overflow formatting doesn't seem to work. Would appreciate if anyone helps.
The classical matrix multiplication by divide and conquer would be such an example. The recurrence relation is: T(n)=8T(n/2)+ Theta(n^2). Another would be Straussen algorithm.
Math notation is (sadly) limited to only a few stackexchange sites.
Given a of 6sided pair of dices, in how many ways can I get sum K.
Can someone give a detailed explanation to me how to use dynamic programming to solve this problem.
I have done reading on it but still hard to understand link