 # Understanding Mo's Algorithm

Firstly, Mo’s Algorithm uses offline queries to its advantage. If you don’t know about offline queries, they are basically inputting the queries into a vector/list and then sorting them in your way, and finding the answers of each using the previously answered queries.

Prerequisites: None

I will explain the question with a problem.

Problem:
You are given an array A of N elements. You have to answer Q queries of type l, r. For each query you need to find the sum of elements from A_l to A_r and output it.

Naive Implementation:
What would be a naive way to do this question? Probably take each query, and iterate from l to r and print the sum. But, for large values of N and Q this will time out.

Its complexity is O(N * Q), which is pretty bad because you can’t work with values like N = 10^5 and Q = 10^5. Let us try an think of a solution which is faster than this.

Segment Tree?

Yes, this can work here, giving us the time complexity of O(N log N), but it is very complex and beginners can’t do this question with that. Also, there are many blogs about that, so I am not going into this.

I need something which has the time complexity less than O(N * Q) and does not involve and data structure except the basic ones.

This is where Mo’s Algorithm comes to play.

Idea: We can use the answer for 1 query for the next.
Is this possible? Yes it is.

So, we have an array A[1, 2, .., N] and a vector of pairs, Queries[1, 2, 3, ..Q]

Lets divide the array A into sqrt(N) blocks.

Thus, the size of each block will be sqrt(N). It is easy to prove, because sqrt(N) * sqrt(N) = N. Our main aim is to answer queries in the first block first, then go to the second block and so on. Thus, each l and r in the query has to fall in one of these blocks. So we sort all queries from (1...sqrt(N) - 1), (sqrt(N)...2 * sqrt(N) - 1) and so on.

So, we sort the queries on basis of the block where l lies, and then sort in increasing order of r.

Note: It might happen that queries are sorted in a way that l is not increasing, that is because we are sorting l by blocks only. Then it depends on the value of r.

Now, we maintain sum and two pointers currL and currR.
sum stores the answer for segment currL to currR, and currL and currR are the left and right index respectively.

When moving from the ith query to the i + 1th query, we compare Queries[i].left and currL. Then we increment or decrement currL by one and change sum accordingly. Similarly we compare Queries[i].right and currR and change things accordingly.

Implementation:

``````//other stuff to be done here. This is just for understanding sorting
//and the implementation of Mo's Algorithm
long long block;

struct queries {
long long l, r, idx, ans;
};

vector<queries> query;

bool cmp(queries a, queries b) {
if((a.l / block) != (b.l / block))
return ((a.l / block) < (b.l / block));
return a.r < b.r;
}

int main() {
//input to be taken
block = (long long)(sqrt(n));
sort(query.begin(), query.begin() + q, cmp); //query is the array for queries
//q is the number of queries
long long currL = 1, currR = n, sum = 0;
for(long long i = 0; i < query.size(); i++) {
long long l = query[i].l, r = query[i].r;
while(currL < l) {
sum -= a[currL];
currL++;
}
while(currL > l) {
currL--;
sum += a[currL];
}
while(currR < r) {
currR++;
sum += a[currR];
}
while(currR > r) {
sum -= a[currR];
currR--;
}
query[i].ans = sum;
}
//now we sort the queries by indexes to give the answer to the corresponding query
//output the answer to the corresponding query
}
``````

Time Complexity:
O(N * sqrt(N))

How much currR is moved?
For each block, queries are sorted in increasing order of r. So, for a block, currR moves in increasing order. In worst case, before beginning of every block, currR at extreme right and current block moves it back the extreme left. This means that for every block, currR moves at most O(N) . Since there are O(sqrt(N)) blocks, total movement of currR is O(N * sqrt(N)) .

How much currL is moved?
Since all queries are sorted in a way that L values are grouped by blocks, movement is O(sqrt(N)) when we move from one query to another query. For Q queries, movement is O(Q *sqrt(N))

Thus, time complexity is O((N + Q) * sqrt(N)) which is equivalent to O(N * sqrt(N))

Resources:
These are to help you where you get stuck, they are beginner-friendly.

P.S: There are some problems which can be done with Segment Tree too, but some which have to be done by Mo’s Algorithm

Example Question: https://codeforces.com/contest/86/problem/D

Practice Questions:

If you have any queries, please comment down, I will try to answer them asap!

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