QED
Linear Algebra · step 3 of 13

Determinants

The determinant is a single number measuring how a matrix scales volume, and it is zero exactly when the matrix is singular. For 2×2 it is ad − bc; for larger matrices, cofactor expansion along a row or column works, but row reduction to triangular form is faster since a triangular determinant is just the product of the diagonal.

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Method: how to approach it

The order below is what examiners expect to see, and each step carries its own marks.

  1. Use ad − bc for 2×2The base case for every cofactor expansion.
  2. Expand along the row or column with most zerosEach zero entry kills a whole cofactor, so choosing well can halve the work.
  3. Get the sign pattern rightCofactor signs alternate in a checkerboard starting + at the top left: (−1)^(i+j).
  4. Or row reduce to triangularAdding a multiple of one row to another leaves the determinant unchanged; swapping rows negates it; scaling a row scales it.

Worked example

Compute the determinant of [[2, 0, 1], [3, −1, 2], [1, 4, 0]].

  1. Expand along the first row, which contains a zero.
  2. 2·det[[−1,2],[4,0]] − 0·(…) + 1·det[[3,−1],[1,4]].
  3. First 2×2: (−1)(0) − (2)(4) = −8. Third: (3)(4) − (−1)(1) = 13.
  4. Total: 2(−8) + 1(13) = −16 + 13.

Answer. −3. Non-zero, so the matrix is invertible.

Where marks get dropped

These are the specific errors that cost credit on determinants questions — QED's rubric penalises each of them separately.

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Determinants — frequently asked questions

What does the determinant mean geometrically?

The signed volume scale factor of the linear map. A determinant of 0 means the map collapses space into a lower dimension, which is why it detects singularity.

Is det(AB) = det A · det B?

Yes, always. It follows that det(A⁻¹) = 1/det A and that similar matrices have equal determinants.

Which method is fastest?

For 2×2 and 3×3, direct expansion. For 4×4 and beyond, row reduce to triangular form — cofactor expansion costs O(n!) while elimination costs O(n³).

The rest of Linear Algebra

Matrices, systems, determinants, eigenvalues. Each subtopic below has its own method, worked example and mark-losing traps.

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