How do you solve First Completely Painted Row or Column on LeetCode?

First Completely Painted Row or Column (LeetCode #2661) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Using a mapping allows for quick access to matrix positions, improving efficiency.

What is the time complexity of First Completely Painted Row or Column?

The optimal solution for First Completely Painted Row or Column runs in O(n) time and uses O(n) space. This complexity is optimal because we only traverse the array and matrix a limited number of times, leading to a linear time complexity.

What is the brute force solution for First Completely Painted Row or Column?

The brute force approach for First Completely Painted Row or Column is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves checking each time we paint a cell if any row or column is completely painted. This is straightforward but inefficient as it checks all rows and columns repeatedly. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve First Completely Painted Row or Column?

First Completely Painted Row or Column uses the following concepts: Array, Hash Table, Matrix. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for First Completely Painted Row or Column?

Always consider how to reduce the number of checks you perform. Think about how you can preprocess data to make your solution more efficient. Practice explaining your thought process clearly, as communication is key in interviews.

What are common mistakes when solving First Completely Painted Row or Column?

Not using a mapping for positions, leading to inefficient searches. Failing to check both row and column counts after each paint.

#2661

First Completely Painted Row or Column

Medium

ArrayHash TableMatrixHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

The optimal solution uses a mapping of values to their positions in the matrix, allowing us to efficiently track painted cells and check for complete rows or columns without repeatedly scanning the matrix.

⚙️

Algorithm

4 steps

1Step 1: Create a mapping of each value in mat to its coordinates.
2Step 2: Initialize counters for rows and columns to track painted counts.
3Step 3: For each index in arr, use the mapping to find the position and update the counters.
4Step 4: After each update, check if any row or column has been fully painted.

solution.py14 lines

1# Full working Python code
2class Solution:
3    def firstCompleteIndex(self, arr, mat):
4        m, n = len(mat), len(mat[0])
5        position = {mat[r][c]: (r, c) for r in range(m) for c in range(n)}
6        row_count = [0] * m
7        col_count = [0] * n
8        for i, num in enumerate(arr):
9            r, c = position[num]
10            row_count[r] += 1
11            col_count[c] += 1
12            if row_count[r] == n or col_count[c] == m:
13                return i
14        return -1

ℹ

Complexity note: This complexity is optimal because we only traverse the array and matrix a limited number of times, leading to a linear time complexity.

1Using a mapping allows for quick access to matrix positions, improving efficiency.
2Tracking painted counts for rows and columns can help determine completion without scanning the entire matrix.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.