Row Operations and Determinant Changes | Generated by AI

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That’s a great question! Let’s address the row operations and their effect on the determinant.

📐 Achieving Row Echelon Form (REF)

You are correct that swapping rows, multiplying a row by a scalar, and adding a multiple of one row to another are the three elementary row operations used to achieve Row Echelon Form (REF).


🛑 Effect of Row Operations on the Determinant

Your statement that the determinant of a matrix doesn’t change during these operations is partially incorrect. Only one of the three elementary row operations leaves the determinant unchanged:

1. Adding a Multiple of One Row to Another

2. Multiplying a Row by a Non-Zero Scalar

3. Swapping Two Rows


💡 Summary Table

Elementary Row Operation Notation Effect on Determinant
Row Swap \(R_i \leftrightarrow R_j\) \(\det(A’) = -\det(A)\)
Scalar Multiplication \(R_i \leftarrow c R_i\) \(\det(A’) = c \cdot \det(A)\)
Row Addition \(R_i \leftarrow R_i + c R_j\) \(\det(A’) = \det(A)\)

If you use row operations to transform a matrix \(A\) into its Row Echelon Form \(U\), you can find the original determinant by keeping track of the changes:

\(\)\det(A) = (-1)^s \cdot \frac{1}{c_1 c_2 \dots c_k} \cdot \det(U)\(\)

where \(s\) is the number of row swaps and \(c_1, c_2, \dots, c_k\) are the scalars used in row multiplications. For a matrix in REF, \(\det(U)\) is simply the product of its diagonal entries (since it’s an upper triangular matrix).


Would you like to try using these operations on a specific matrix to find its determinant?


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