Prerequisite – Fenwick Tree

We know that to answer range sum queries on a 1-D array efficiently, binary indexed tree (or Fenwick Tree) is the best choice (even better than segment tree due to less memory requirements and a little faster than segment tree).

**Can we answer sub-matrix sum queries efficiently using Binary Indexed Tree ?**

The answer is **yes**. This is possible using a ** 2D BIT ** which is nothing but an array of 1D BIT.

**Algorithm:**

We consider the below example. Suppose we have to find the sum of all numbers inside the highlighted area-

We assume the origin of the matrix at the bottom – O.Then a 2D BIT exploits the fact that-

Sum under the marked area = Sum(OB) - Sum(OD) - Sum(OA) + Sum(OC)

In our program, we use the getSum(x, y) function which finds the sum of the matrix from (0, 0) to (x, y).

Hence the below formula :

Sum under the marked area = Sum(OB) - Sum(OD) - Sum(OA) + Sum(OC) The above formula gets reduced to, Query(x1,y1,x2,y2) = getSum(x2, y2) - getSum(x2, y1-1) - getSum(x1-1, y2) + getSum(x1-1, y1-1)

where,

**x1, y1 ** = x and y coordinates of C

**x2, y2** = x and y coordinates of B

The updateBIT(x, y, val) function updates all the elements under the region – (x, y) to (N, M) where,

**N ** = maximum X co-ordinate of the whole matrix.

**M** = maximum Y co-ordinate of the whole matrix.

The rest procedure is quite similar to that of 1D Binary Indexed Tree. Below is the C++ implementation of 2D indexed tree

/* C++ program to implement 2D Binary Indexed Tree 2D BIT is basically a BIT where each element is another BIT. Updating by adding v on (x, y) means it's effect will be found throughout the rectangle [(x, y), (max_x, max_y)], and query for (x, y) gives you the result of the rectangle [(0, 0), (x, y)], assuming the total rectangle is [(0, 0), (max_x, max_y)]. So when you query and update on this BIT,you have to be careful about how many times you are subtracting a rectangle and adding it. Simple set union formula works here. So if you want to get the result of a specific rectangle [(x1, y1), (x2, y2)], the following steps are necessary: Query(x1,y1,x2,y2) = getSum(x2, y2)-getSum(x2, y1-1) - getSum(x1-1, y2)+getSum(x1-1, y1-1) Here 'Query(x1,y1,x2,y2)' means the sum of elements enclosed in the rectangle with bottom-left corner's co-ordinates (x1, y1) and top-right corner's co-ordinates - (x2, y2) Constraints -> x1<=x2 and y1<=y2 /\ y | | --------(x2,y2) | | | | | | | | | | --------- | (x1,y1) | |___________________________ (0, 0) x--> In this progrm we have assumed a square matrix. The program can be easily extended to a rectangular one. */ #include<bits/stdc++.h> using namespace std; #define N 4 // N-->max_x and max_y // A structure to hold the queries struct Query { int x1, y1; // x and y co-ordinates of bottom left int x2, y2; // x and y co-ordinates of top right }; // A function to update the 2D BIT void updateBIT(int BIT[][N+1], int x, int y, int val) { for (; x <= N; x += (x & -x)) { // This loop update all the 1D BIT inside the // array of 1D BIT = BIT[x] for (; y <= N; y += (y & -y)) BIT[x][y] += val; } return; } // A function to get sum from (0, 0) to (x, y) int getSum(int BIT[][N+1], int x, int y) { int sum = 0; for(; x > 0; x -= x&-x) { // This loop sum through all the 1D BIT // inside the array of 1D BIT = BIT[x] for(; y > 0; y -= y&-y) { sum += BIT[x][y]; } } return sum; } // A function to create an auxiliary matrix // from the given input matrix void constructAux(int mat[][N], int aux[][N+1]) { // Initialise Auxiliary array to 0 for (int i=0; i<=N; i++) for (int j=0; j<=N; j++) aux[i][j] = 0; // Construct the Auxiliary Matrix for (int j=1; j<=N; j++) for (int i=1; i<=N; i++) aux[i][j] = mat[N-j][i-1]; return; } // A function to construct a 2D BIT void construct2DBIT(int mat[][N], int BIT[][N+1]) { // Create an auxiliary matrix int aux[N+1][N+1]; constructAux(mat, aux); // Initialise the BIT to 0 for (int i=1; i<=N; i++) for (int j=1; j<=N; j++) BIT[i][j] = 0; for (int j=1; j<=N; j++) { for (int i=1; i<=N; i++) { // Creating a 2D-BIT using update function // everytime we/ encounter a value in the // input 2D-array int v1 = getSum(BIT, i, j); int v2 = getSum(BIT, i, j-1); int v3 = getSum(BIT, i-1, j-1); int v4 = getSum(BIT, i-1, j); // Assigning a value to a particular element // of 2D BIT updateBIT(BIT, i, j, aux[i][j]-(v1-v2-v4+v3)); } } return; } // A function to answer the queries void answerQueries(Query q[], int m, int BIT[][N+1]) { for (int i=0; i<m; i++) { int x1 = q[i].x1 + 1; int y1 = q[i].y1 + 1; int x2 = q[i].x2 + 1; int y2 = q[i].y2 + 1; int ans = getSum(BIT, x2, y2)-getSum(BIT, x2, y1-1)- getSum(BIT, x1-1, y2)+getSum(BIT, x1-1, y1-1); printf ("Query(%d, %d, %d, %d) = %d\n", q[i].x1, q[i].y1, q[i].x2, q[i].y2, ans); } return; } // Driver program int main() { int mat[N][N] = {{1, 2, 3, 4}, {5, 3, 8, 1}, {4, 6, 7, 5}, {2, 4, 8, 9}}; // Create a 2D Binary Indexed Tree int BIT[N+1][N+1]; construct2DBIT(mat, BIT); /* Queries of the form - x1, y1, x2, y2 For example the query- {1, 1, 3, 2} means the sub-matrix- y /\ 3 | 1 2 3 4 Sub-matrix 2 | 5 3 8 1 {1,1,3,2} ---> 3 8 1 1 | 4 6 7 5 6 7 5 0 | 2 4 8 9 | --|------ 0 1 2 3 ----> x | Hence sum of the sub-matrix = 3+8+1+6+7+5 = 30 */ Query q[] = {{1, 1, 3, 2}, {2, 3, 3, 3}, {1, 1, 1, 1}}; int m = sizeof(q)/sizeof(q[0]); answerQueries(q, m, BIT); return(0); }

Output:

Query(1, 1, 3, 2) = 30 Query(2, 3, 3, 3) = 7 Query(1, 1, 1, 1) = 6

**Time Complexity:**

- Both updateBIT(x, y, val) function and getSum(x, y) function takes O(log(NM)) time.
- Building the 2D BIT takes O(NM log(NM)).
- Since in each of the queries we are calling getSum(x, y) function so answering all the Q queries takes
**O(Q.log(NM))**time.

Hence the overall time complexity of the program is **O((NM+Q).log(NM))** where,

N = maximum X co-ordinate of the whole matrix.

M = maximum Y co-ordinate of the whole matrix.

Q = Number of queries.

**Auxiliary Space: ** O(NM) to store the BIT and the auxiliary array

**References: **https://www.topcoder.com/community/data-science/data-science-tutorials/binary-indexed-trees/

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