Understanding the Problem
We are given a square matrix and asked to rotate it 90 degrees clockwise, in-place, meaning we cannot use any additional memory. Rotating the matrix means repositioning its elements such that each element moves to its new position as if the matrix was physically rotated.
Let’s break down what “rotating 90 degrees clockwise” actually means. Imagine rotating a square paper 90 degrees to the right — the top row becomes the rightmost column, the second row becomes the second-to-right column, and so on.
Our goal is to do this transformation using a step-by-step approach that is easy to understand and efficient to implement without using extra space.
Step-by-Step Solution with Example
Step 1: Start with an Example
Consider the following 3x3 matrix:
1 2 3
4 5 6
7 8 9
After rotating 90 degrees clockwise, the matrix should become:
7 4 1
8 5 2
9 6 3
Step 2: Transpose the Matrix
Transposing means converting rows to columns. This is done by swapping elements matrix[i][j]
with matrix[j][i]
where i < j
.
Transpose of the original matrix:
1 4 7
2 5 8
3 6 9
Step 3: Reverse Each Row
After the transpose, we reverse each row to get the final rotated matrix:
- Row 1:
1 4 7
→ 7 4 1
- Row 2:
2 5 8
→ 8 5 2
- Row 3:
3 6 9
→ 9 6 3
Final Matrix:
7 4 1
8 5 2
9 6 3
Step 4: Why This Works
The transpose step turns the matrix’s rows into columns. The reverse step adjusts the order of elements to simulate a 90-degree clockwise rotation. These two operations combined perform the required rotation — efficiently and in-place.
Edge Cases
- Empty Matrix: An empty matrix should return as-is. There’s nothing to rotate.
- 1x1 Matrix: Rotating a single element matrix has no visible effect. It remains unchanged.
- Non-Square Matrix: This solution only works for square matrices (n x n). Applying it to a non-square matrix (e.g., 3x2) will lead to incorrect behavior and should be avoided.
Finally
This in-place method is simple and elegant. It uses two main operations — transpose and reverse — to achieve the rotation. It runs in O(n²) time and uses O(1) extra space, making it optimal for square matrices.
Always validate the input to make sure the matrix is square and not empty, and handle edge cases gracefully. Understanding the transformation steps visually with an example is key to mastering matrix rotation problems.
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