How do you solve Equal Row and Column Pairs on LeetCode?

Equal Row and Column Pairs (LeetCode #2352) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n²) time complexity and O(n) space complexity. Key insight: Using a HashMap can significantly reduce the number of comparisons needed.

What is the brute force solution for Equal Row and Column Pairs?

The brute force approach for Equal Row and Column Pairs is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force method involves comparing each row with each column directly. For every row, we check every column to see if they are equal, which is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n²).

What algorithm or data structure is used to solve Equal Row and Column Pairs?

Equal Row and Column Pairs uses the following concepts: Array, Hash Table, Matrix, Simulation. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Equal Row and Column Pairs?

Always clarify the problem constraints and edge cases. Think about how to optimize your solution before coding. Discuss your thought process while coding to demonstrate your understanding.

What are common mistakes when solving Equal Row and Column Pairs?

Not considering the order of elements when comparing rows and columns. Forgetting to convert columns to a comparable format.

#2352

Equal Row and Column Pairs

Medium

ArrayHash TableMatrixSimulationHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

Time O(n²)Space O(n)

Instead of comparing each row with each column directly, we can use a HashMap to store the frequency of each row. Then, we can check how many columns match these rows using the same HashMap, which significantly reduces the number of comparisons.

⚙️

Algorithm

3 steps

1Step 1: Create a HashMap to count occurrences of each row as a string.
2Step 2: For each column, convert it to a string and check if it exists in the HashMap.
3Step 3: Sum the counts from the HashMap for matching columns.

solution.py10 lines

1from collections import Counter
2
3def equalPairs(grid):
4    n = len(grid)
5    row_count = Counter(tuple(row) for row in grid)
6    count = 0
7    for c in range(n):
8        column = tuple(grid[r][c] for r in range(n))
9        count += row_count[column]
10    return count

ℹ

Complexity note: The time complexity remains O(n²) due to the need to process each element in the grid. However, we use O(n) space for the HashMap to store row frequencies, which allows us to quickly check for matching columns.

1Using a HashMap can significantly reduce the number of comparisons needed.
2String representations of rows and columns simplify equality checks.

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