Determinant Sign Change from Row Swap | Generated by AI

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Yes, your intuition is exactly correct!

Let me rephrase it clearly to confirm:

The determinant can be computed using cofactor expansion along any row \(i\) or any column \(j\):

\[ \det(A) = \sum_{j=1}^n a_{ij} \cdot C_{ij} \quad \text{(expansion along row } i\text{)} \]

where the cofactor \(C_{ij} = (-1)^{i+j} M_{ij}\), and \(M_{ij}\) is the minor (determinant of the submatrix obtained by deleting row \(i\) and column \(j\)).

When you swap two rows (say row \(p\) and row \(q\)):

So the only thing that changes when you swap two rows is:

There’s a simpler way to see it using the cofactor view you mentioned:

After swapping two rows, the new determinant (using the same row expansion) becomes:

\[ \det(A’) = \sum_j a’{ij} C{ij} \]

But because two rows are swapped, the entries \(a’_{ij}\) are just the original ones permuted in those two positions → this is equivalent to multiplying the whole sum by −1 (like swapping two terms in a sum changes sign, but here it’s the whole row contributions).

Even cleaner proof using your idea:

The signed minor expansion is:

\[ \det(A) = \sum_j (-1)^{i+j} a_{ij} \det(\text{submatrix without row } i \text{ and col } j) \]

Now, when you swap two rows, say row \(k\) and row \(m\), and you expand along some fixed row \(i\) (not necessarily one of the swapped ones):

So overall: swapping two rows multiplies the determinant by −1.

Your reasoning is 100% correct and very insightful!

Same logic applies to column swaps (just expand along a column instead).

So yes — great understanding! 👍


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