# 304.Range Sum Query 2D - Immutable (M)

Given a 2D matrix `matrix`, handle multiple queries of the following type:

* Calculate the **sum** of the elements of `matrix` inside the rectangle defined by its **upper left corner** `(row1, col1)` and **lower right corner** `(row2, col2)`.

Implement the NumMatrix class:

* `NumMatrix(int[][] matrix)` Initializes the object with the integer matrix `matrix`.
* `int sumRegion(int row1, int col1, int row2, int col2)` Returns the **sum** of the elements of `matrix` inside the rectangle defined by its **upper left corner** `(row1, col1)` and **lower right corner** `(row2, col2)`.

 

**Example 1:**

![](https://assets.leetcode.com/uploads/2021/03/14/sum-grid.jpg)

```
Input
["NumMatrix", "sumRegion", "sumRegion", "sumRegion"]
[[[[3, 0, 1, 4, 2], [5, 6, 3, 2, 1], [1, 2, 0, 1, 5], [4, 1, 0, 1, 7], [1, 0, 3, 0, 5]]], [2, 1, 4, 3], [1, 1, 2, 2], [1, 2, 2, 4]]
Output
[null, 8, 11, 12]

Explanation
NumMatrix numMatrix = new NumMatrix([[3, 0, 1, 4, 2], [5, 6, 3, 2, 1], [1, 2, 0, 1, 5], [4, 1, 0, 1, 7], [1, 0, 3, 0, 5]]);
numMatrix.sumRegion(2, 1, 4, 3); // return 8 (i.e sum of the red rectangle)
numMatrix.sumRegion(1, 1, 2, 2); // return 11 (i.e sum of the green rectangle)
numMatrix.sumRegion(1, 2, 2, 4); // return 12 (i.e sum of the blue rectangle)
```

 

**Constraints:**

* `m == matrix.length`
* `n == matrix[i].length`
* `1 <= m, n <= 200`
* `-105 <= matrix[i][j] <= 105`
* `0 <= row1 <= row2 < m`
* `0 <= col1 <= col2 < n`
* At most `104` calls will be made to `sumRegion`.

### Solution:

比如说输入的 `matrix` 如下图：

[![](https://labuladong.github.io/algo/images/%E5%89%8D%E7%BC%80%E5%92%8C/4.png)](https://labuladong.github.io/algo/images/%E5%89%8D%E7%BC%80%E5%92%8C/4.png)

那么 `sumRegion([2,1,4,3])` 就是图中红色的子矩阵，你需要返回该子矩阵的元素和 8。

这题的思路和一维数组中的前缀和是非常类似的，如下图：

[![](https://labuladong.github.io/algo/images/%E5%89%8D%E7%BC%80%E5%92%8C/5.png)](https://labuladong.github.io/algo/images/%E5%89%8D%E7%BC%80%E5%92%8C/5.png)

如果我想计算红色的这个子矩阵的元素之和，可以用绿色矩阵减去蓝色矩阵减去橙色矩阵最后加上粉色矩阵，而绿蓝橙粉这四个矩阵有一个共同的特点，就是左上角就是 `(0, 0)` 原点。

那么我们可以维护一个二维 `preSum` 数组，专门记录以原点为顶点的矩阵的元素之和，就可以用几次加减运算算出任何一个子矩阵的元素和：

```java
class NumMatrix {
    // preSum[i][j] 记录矩阵 [0, 0, i, j] 的元素和
    private int[][] preSum;
    
    public NumMatrix(int[][] matrix) {
        int m = matrix.length, n = matrix[0].length;
        if (m == 0 || n == 0) return;
        // 构造前缀和矩阵
        preSum = new int[m + 1][n + 1];
        for (int i = 1; i <= m; i++) {
            for (int j = 1; j <= n; j++) {
                // 计算每个矩阵 [0, 0, i, j] 的元素和
                preSum[i][j] = preSum[i-1][j] + preSum[i][j-1] + matrix[i - 1][j - 1] - preSum[i-1][j-1];
            }
        }
    }
    
    // 计算子矩阵 [x1, y1, x2, y2] 的元素和
    public int sumRegion(int x1, int y1, int x2, int y2) {
        // 目标矩阵之和由四个相邻矩阵运算获得
        return preSum[x2+1][y2+1] - preSum[x1][y2+1] - preSum[x2+1][y1] + preSum[x1][y1];
    }
}
```

这样，`sumRegion` 函数的复杂度也用前缀和技巧优化到了 O(1)。


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