Блог пользователя anh1l1ator

Автор anh1l1ator, 9 лет назад, По-английски

So , formally the problem statement :

You are given a sequence A of N (N≤250000) integers between 1 and 50000. On this sequence you have to apply M (M≤10000) operations of the form: modify the i-th element in the sequence and then say how many inversions are there in the sequence. The number of inversions in a sequence is given by the number of pairs (i,j) with i < j and Ai > Aj.

My approach is to , 1) Divide the array into square root pieces , and maintain a fenwick tree for each of the blocks that stores 1 corresponding to each number in that block ( For querying number of numbers lesser than a particular value in that block )

2) Count the inversions .

3) Now whenever Update comes , I update get inversions from all blocks except the index's box(I count the difference between the inversions due to the new value — inversions due to old value and add it to the inversions) and add it ! Plus I traverse the block of index ! Total Query time O( Sqquare root of N * log 50000 ) .

It is giving TLE Even with fast scanning and outputting Here is my code , can it be further optimised ? Ideone

Link to problem Spoj

Is there any other approach to it ?

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9 лет назад, # |
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For each query you should find: in how many inversions i-th number participed before and new number of inversions for updated number.Total number of inversions will change for difference of this two number. So you should count in each query how many numbers are bigger left of a[i] and how many numbers are smaller right of a[i]. You can do this in sqrt(n)-easy dp. Dp[i][j] count the number of smaller numbers than j in i-th block.After query update dp for block where is placed i-th number. Total complexity O( n sqrt(n)).

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    9 лет назад, # ^ |
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    To update DP[i][j] after each update it will take j operations where j can be at max 50000. Now to speed up this procedure I have used a series of fenwick trees (for each square root block a different one ) which can cause fast retrieval and fast updation but the problem is it is timing out!!

    As your state is Dp[i][j] count the number of smaller numbers than j in i-th block

    I dont think there is someway to update it faster than log N . Please see my code !!

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      9 лет назад, # ^ |
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      My bad, you are right ( I see three ziroes in max number, not four :D ). I can make it a little better ( maybe):

      You can sort all the blocks and after that to find number of values bigger and smaller than a[i] in every block you can run binary search. Complexity of that program is O(m sqrt(n) log (sqrt(n))).

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        9 лет назад, # ^ |
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        Happens with everyone :D And I will try it once I get AC , coz I dont think there is much difference as the maximum value of elements is lesser than max of N . Plus BIT doesn't have a big constant

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9 лет назад, # |
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I recently solved this problem using sqrt decomposition , i got TLE first when i had block size as 500 , but then i tried some other things like trie + treap / trie + segtree , all TLEd then finally i went back to sqrt decomposition and kept block size as 1000 and added fast input and it gave AC .

Final AC code with sqrt decomp

Segtree + Trie (TLE)

Treap + Trie (TLE)

TLE sqrt decomp

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    9 лет назад, # ^ |
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    Are you talking about Mo's algorithm ? What have you done ? I think your solution is quite close to mine!

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      9 лет назад, # ^ |
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      I do not know dude , when I change my block size to 1005 my code gets AC -_- !!

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        9 лет назад, # ^ |
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        And I didnt even knew that it is a standard type of algorithm known as "SQuare root decomposition"

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    9 лет назад, # ^ |
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    Do you know of a problem in which the expected solution involves a Treap + Trie or a Segment Tree with a Treap for each node?
    I'd really like to solve such a question :P

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    9 лет назад, # ^ |
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    Same happened with me. Changing block size to 1000 from 500 worked. I have analyzed the complexity. This is why it worked

    For each query, For 500 block size complexity= O(500+ 500 * log(500))=O(5000) For 1000 block size complexity= O(1000+ 250 *log(1000))=O(3500)

    Therefore 1000 works

    Edit: Corrected my mistake.

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      9 лет назад, # ^ |
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      I believe O(1009) makes no sence btw

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        9 лет назад, # ^ |
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        Oh Sorry My bad. It was 500*log(500) so O(5000) for the 500 block and O(3500) for the 1000 block.

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          9 лет назад, # ^ |
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          i mean you dont use O() like that, it has to have some function of N inside brackets. at least i believe in it

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          9 лет назад, # ^ |
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          I think there exists some better algorithm for this task seeing multiple solutions with very small running time

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9 лет назад, # |
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you can use segment tree with an ordered multiset(also you can compress numbers first and use fenwick that is faster) on each node. so we can answere each query in O(lg(n)^2)

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    9 лет назад, # ^ |
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    Yeah this is a very easy solution , I probably didn't ever knew anything as segment tree with sets that's why i wrote such a bad solution :D

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9 лет назад, # |
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My implementation. Sqrt-Decomposition + BIT. Time : 1.1 Seconds (before they changed the processors). 0.8 seconds on Cube.

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7 лет назад, # |
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I wrote many solutions and I got two of them accepted:

Nested Trie

Trie + sqrt(n) decomp