LeetCode 2536. Increment Submatrices by One Solution in Java, C++, Python & More | Explanation + Code

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2536. Increment Submatrices by One

Description

You are given a positive integer n, indicating that we initially have an n x n 0-indexed integer matrix mat filled with zeroes.

You are also given a 2D integer array query. For each query[i] = [row1i, col1i, row2i, col2i], you should do the following operation:

  • Add 1 to every element in the submatrix with the top left corner (row1i, col1i) and the bottom right corner (row2i, col2i). That is, add 1 to mat[x][y] for all row1i <= x <= row2i and col1i <= y <= col2i.

Return the matrix mat after performing every query.

 

Example 1:

Input: n = 3, queries = [[1,1,2,2],[0,0,1,1]]
Output: [[1,1,0],[1,2,1],[0,1,1]]
Explanation: The diagram above shows the initial matrix, the matrix after the first query, and the matrix after the second query.
- In the first query, we add 1 to every element in the submatrix with the top left corner (1, 1) and bottom right corner (2, 2).
- In the second query, we add 1 to every element in the submatrix with the top left corner (0, 0) and bottom right corner (1, 1).

Example 2:

Input: n = 2, queries = [[0,0,1,1]]
Output: [[1,1],[1,1]]
Explanation: The diagram above shows the initial matrix and the matrix after the first query.
- In the first query we add 1 to every element in the matrix.

 

Constraints:

  • 1 <= n <= 500
  • 1 <= queries.length <= 104
  • 0 <= row1i <= row2i < n
  • 0 <= col1i <= col2i < n

Solutions

Solution 1: 2D Difference Array

A 2D difference array is a technique used to efficiently handle range updates on 2D arrays. We can implement fast updates on submatrices by maintaining a difference matrix of the same size as the original matrix.

Suppose we have a 2D difference matrix diff, initially with all elements set to 0. For each query [row1, col1, row2, col2], we can update the difference matrix through the following steps:

  1. Increment position (row1, col1) by 1.
  2. Decrement position (row2 + 1, col1) by 1, provided that row2 + 1 < n.
  3. Decrement position (row1, col2 + 1) by 1, provided that col2 + 1 < n.
  4. Increment position (row2 + 1, col2 + 1) by 1, provided that row2 + 1 < n and col2 + 1 < n.

After completing all queries, we need to convert the difference matrix back to the original matrix using prefix sums. That is, for each position (i, j), we calculate:

mat[i][j] = diff[i][j] + (mat[i-1][j] if i > 0 else 0) + (mat[i][j-1] if j > 0 else 0) - (mat[i-1][j-1] if i > 0 and j > 0 else 0)

The time complexity is O(m + n2), where m and n are the length of queries and the given n, respectively. Ignoring the space consumed by the answer, the space complexity is O(1).

PythonJavaC++GoTypeScriptRustC#
class Solution: def rangeAddQueries(self, n: int, queries: List[List[int]]) -> List[List[int]]: mat = [[0] * n for _ in range(n)] for x1, y1, x2, y2 in queries: mat[x1][y1] += 1 if x2 + 1 < n: mat[x2 + 1][y1] -= 1 if y2 + 1 < n: mat[x1][y2 + 1] -= 1 if x2 + 1 < n and y2 + 1 < n: mat[x2 + 1][y2 + 1] += 1 for i in range(n): for j in range(n): if i: mat[i][j] += mat[i - 1][j] if j: mat[i][j] += mat[i][j - 1] if i and j: mat[i][j] -= mat[i - 1][j - 1] return mat(code-box)

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