### code_fille's blog

By code_fille, history, 3 years ago, ,

Given an array of integers and a range find the count of subarrays whose sum lies in the given range. Do so in less than O(n^2) complexity.

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 » 3 years ago, # | ← Rev. 6 →   +10 let us call the range [ l, r ] build the prefix sum array for the given array, let us call it B (B[0]=0 for empty prefix) nowB[i] - B[j] where 0 ≤ j < igives all the sums of subarrays that end in ith element So, in order for the sum of subarray [ j + 1...i ] to be between l and r the following should be true: l ≤ B[i] - B[j] ≤ r => l - B[i] ≤  - B[j] ≤ r - B[i] => B[i] - l ≥ B[j] ≥ B[i] - r so for each B[i], we should find the number of B[j] s that are in the previous range and j < i, which is done in a segment tree total complexity: O(nlogn) NOTE If the values are all positive you can use binary search for O(nlogn)
•  » » 3 years ago, # ^ |   0 How could segment tree be used to find the possible values of j?
•  » » » 3 years ago, # ^ | ← Rev. 3 →   0 Array C , C[i] is the number of B[j]'s which are equal to iYou need a segment tree to perform 2 types of queries:1) add 1 to C[x]2) get the sum of C[x] : s<=x<=e So for each B[i] you query for the sum of C[x] between B[i]-r and B[i]-l Then you add 1 to C[B[i]]
•  » » » » 8 months ago, # ^ |   0 B[i] can be negative ..how it is possible to store in array?
•  » » » » » 8 months ago, # ^ |   0 Then compress the values
•  » » » » » » 8 months ago, # ^ |   0 I was trying to solve this question. I tried to compress the values. But it is giving WA. Here is the code. Please help me.
•  » » » » » » » 8 months ago, # ^ |   0 You will have at most N+2 different values (Including L and R), compress them.
•  » » » » » » » » 8 months ago, # ^ |   0 Can you please have a glance at my code?
•  » » » » » » » » 8 months ago, # ^ |   0 For each B[i], I have to put B[i]-l and B[i]-r. I have to compress n*2*n values.
•  » » » » » » » » » 8 months ago, # ^ |   0 Were you able to solve that question ? If yes, can you please share the code? I am also getting W.A.
•  » » » » » » » » » 8 months ago, # ^ |   0 Yes, I solved it Here is the code
•  » » 3 years ago, # ^ | ← Rev. 2 →   0 If the values are all positive you can solve in O(N) using only 3 pointers.Note: You can also solve in O(NlogN) without segment tree, just using divide-and-conquer idea.
•  » » » 3 years ago, # ^ |   0 Could you elaborate how divide and conquer could be used?
•  » » » » 3 years ago, # ^ | ← Rev. 3 →   0 B — partial sum, B[0] = 0. Now we are going to split our verctor a into 2 parts with size N / 2 each.2 cases are possible:1) l, r < N / 2 or l, r > N / 2: We can find the answer recursively for the left part and for the right part.2) l < N / 2 and r > N / 2: We can find the answer in O(N) using 3 pointers but only if the left part sorted and the right part sorted too. But you can maintain these parts sorted by using merge sort algorithm.The algorithm is very similar to the algorithm for counting inversions in O(NlogN) time. You can find the explanation on coursera: link
•  » » 2 years ago, # ^ |   0 For segment tree , Is the time complexity is (n*log(sum of all array elements)) ? correct me if i am wrong.
•  » » » 2 years ago, # ^ |   0 correct me if i am wrong. Wrong? You didn't state anything.
•  » » » » 2 years ago, # ^ |   0 As JoudZouzou said ,when we use segment tree to solve the problem total complexity: O(nlogn) but here the logn is not the size of input but it is log of the sum of the array elements
•  » » 7 months ago, # ^ |   +8 Your solution is quite beautiful :)
 » 2 years ago, # | ← Rev. 2 →   0 Instead of segment tree, binary search can also be used( inbuilt functions lower_bound() and upper_bound()) to solve this problem in O(nlgn). Here is the code in c++: Function to return the count- int numRange(vector &A, int B, int C) { int i,j=0,n=A.size(); //store prefix sum vector psum(n,0); for(i=0;i
•  » » 2 years ago, # ^ |   0 Could you please provide such type of problem for more practice? :)
 » 7 months ago, # | ← Rev. 3 →   0 Note: This works only for positive integers.I think following expression may help: (For a range [L,R]) Number of subarrays having sum less than or equal to R - Number of subarrays having sum less than or equal to L-1 For finding required number of subarrays refer this: Subarrays with sum less than k