Matrix Determinant Calculator (2×2 and 3×3)
The determinant of a square matrix is a single scalar that tells you whether the matrix is invertible (and therefore whether a linear system has a unique solution), and equals the signed area / volume of the parallelogram / parallelepiped spanned by its column vectors. This tool covers the two most-taught cases — 2×2 and 3×3 — using ad − bc and cofactor expansion along the first row, with every intermediate minor shown so you can check homework or hand-worked answers.
Matrix A
Please fill in every cell of the matrix with a valid number.
Determinant det(A)
−2
Invertible (det ≠ 0)
Working
det = a·d − b·c
If det(A) ≠ 0 the matrix is invertible and the linear system A·x = b has a unique solution; det(A) = 0 means the rows/columns are linearly dependent.
Formula
2×2: det = a·d − b·c 3×3 (cofactor expansion along row 1): det = a₁₁·M₁₁ − a₁₂·M₁₂ + a₁₃·M₁₃, where Mᵢⱼ is the determinant of the 2×2 minor obtained by deleting row i and column j.
- · 2×2: det([[a,b],[c,d]]) = a·d − b·c — the difference of the two diagonal products.
- · 3×3 cofactor expansion along row 1: signs alternate +, −, + — det = +a·M₁₁ − b·M₁₂ + c·M₁₃.
- · det(A) ≠ 0 ⇒ the matrix is invertible and A·x = b has a unique solution. det(A) = 0 ⇒ the matrix is singular and its rows / columns are linearly dependent.
- · Swapping any two rows (or columns) flips the sign of the determinant. Scaling one row by k scales det by k. Adding a multiple of one row to another leaves det unchanged.
- · For a triangular or diagonal matrix the determinant equals the product of the diagonal entries.
- · Geometric interpretation: |det(A)| is the area of the parallelogram (2-D) or volume of the parallelepiped (3-D) spanned by the column vectors; the sign indicates orientation.
- · Formulas verified against Strang, Introduction to Linear Algebra, ch. 5, and Anton & Rorres, Elementary Linear Algebra, 11th ed., ch. 2.
Frequently asked
What is the determinant actually used for?
Three common uses: (1) invertibility — det(A) ≠ 0 means A is invertible and A·x = b has a unique solution; otherwise the system has no solution or infinitely many. (2) Cramer's rule — solve small linear systems by determinant ratios, xᵢ = det(Aᵢ) / det(A). (3) Geometry — |det(A)| equals the area of the parallelogram (2×2) or volume of the parallelepiped (3×3) spanned by the column vectors, and the same idea drives the Jacobian in multivariable calculus. For matrices larger than 3×3 the determinant is usually computed via LU decomposition rather than expansion by minors.
Why do the three terms in a 3×3 expansion have signs +, −, +?
The cofactor sign is (−1)^(i+j) with i, j starting from 1. Expanding along the first row fixes i = 1, so the signs follow (−1)^(1+1), (−1)^(1+2), (−1)^(1+3) — i.e. +, −, +. If you expand along the second row instead, the signs are −, +, − (and the determinant value is identical — the answer does not depend on which row or column you pick). This tool always expands along the first row, matching most textbooks.
If the determinant is 0, is the matrix useless?
No. det = 0 means the matrix is singular (non-invertible) — its column (or row) vectors are linearly dependent, so A·x = b cannot be solved by multiplying by A⁻¹. But singular matrices are everywhere useful: projection matrices that map 3-D onto 2-D always have det = 0; the graph Laplacian in graph theory always has det = 0; low-rank approximations in machine learning intentionally produce singular matrices. A zero determinant just says "not invertible," not "not meaningful."
Why does the tool stop at 3×3?
2×2 and 3×3 are by far the most common sizes asked in coursework and the easiest to compute by hand — making them the most useful sizes to double-check with a calculator. Cofactor expansion grows factorially (a 4×4 has 24 terms, a 5×5 has 120), so production code uses LU decomposition or Gaussian elimination in O(n³) time instead. For larger matrices reach for NumPy's np.linalg.det(), MATLAB's det(), or Excel's MDETERM function.
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