Solve 2×2, 3×3, and 4×4 systems of linear equations using Cramer's Rule with determinant calculations, step-by-step Dx/Dy/Dz display, solution verification, and determinant comparison visualization.
Cramer's Rule provides an elegant closed-form solution to systems of n linear equations in n unknowns, expressing each variable as a ratio of determinants. For a system Ax = b, each variable xᵢ = det(Aᵢ) / det(A), where Aᵢ is the matrix A with column i replaced by the constant vector b.
This calculator implements Cramer's Rule for 2×2, 3×3, and 4×4 systems. For each variable, it constructs the modified matrix, computes its determinant, and divides by the main determinant. The step-by-step display shows every determinant calculation so you can follow the method from start to finish.
The tool first checks whether det(A) ≠ 0, which is the necessary and sufficient condition for Cramer's Rule to apply. If det(A) = 0, the system is either inconsistent or has infinitely many solutions, and Cramer's Rule cannot determine which case applies — you would need Gauss-Jordan elimination instead.
Visual comparison bars show the relative magnitudes of each numerator determinant and the main determinant, making it easy to see which variables dominate the solution. A verification table substitutes the computed values back into the original equations to confirm correctness. Presets include well-known textbook systems and edge cases like nearly-singular matrices.
Cramer's Rule requires computing n+1 determinants for an n×n system — even a 3×3 system demands four 3×3 determinants, each with its own cofactor expansion. A single sign or arithmetic mistake invalidates the entire solution. This calculator constructs every modified matrix Aᵢ, computes all determinants, and divides to produce each variable's value, with a verification table that substitutes back into the original equations. It is ideal for algebra students learning determinant-based methods and for validating hand-computed solutions.
xᵢ = det(Aᵢ) / det(A), where Aᵢ is matrix A with column i replaced by b. Requires det(A) ≠ 0.
Result: x = 2, y = 3, z = −1
det(A) = −306. Dₓ = det with col 1 replaced by b = −612, Dᵧ = −918, Dᵤ = 306. So x = −612/−306 = 2, y = −918/−306 = 3, z = 306/−306 = −1.
For a system Ax = b with n equations and n unknowns, Cramer's Rule expresses each variable as xᵢ = det(Aᵢ)/det(A), where Aᵢ is the coefficient matrix A with its i-th column replaced by the constant vector b. The method is a direct consequence of the adjugate formula for the inverse: x = A⁻¹b = adj(A)b / det(A). Each component of adj(A)b produces exactly the numerator determinant det(Aᵢ). The prerequisite is det(A) ≠ 0, which guarantees the system has a unique solution.
While elegant, Cramer's Rule is computationally expensive: computing n+1 determinants via cofactor expansion costs O(n · n!) operations, compared to O(n³) for Gaussian elimination. For systems larger than 4×4, Gaussian elimination or LU decomposition is far more practical. However, Cramer's Rule excels in symbolic computation — when matrix entries are algebraic expressions rather than numbers, it produces clean closed-form solutions. It is also useful for solving systems where only one or two variables are needed, since you can compute just those determinants.
Cramer's Rule is one of the first methods taught in linear algebra because it makes the connection between determinants and linear systems explicit. It shows that each solution variable depends on ALL coefficients and constants, weighted through the determinant structure. The rule also motivates the study of when systems have unique solutions (det ≠ 0) versus when they fail (det = 0, leading to either no solution or infinitely many solutions). Understanding Cramer's Rule deepens intuition about the role of the determinant as a measure of a matrix's invertibility.
Cramer's Rule solves a square linear system Ax = b by expressing each variable as a ratio of two determinants: xᵢ = det(Aᵢ)/det(A), where Aᵢ has column i of A replaced with b. Use this as a practical reminder before finalizing the result.
It fails when det(A) = 0, meaning the system is singular — either inconsistent (no solution) or dependent (infinitely many solutions). The rule cannot distinguish between these two cases.
No. Computing n+1 determinants each of size n×n is O(n·n!) with cofactor expansion. For practical computation, Gaussian elimination at O(n³) is far more efficient. Cramer's Rule is mainly of theoretical importance.
No. Cramer's Rule requires exactly n equations in n unknowns (a square coefficient matrix). For non-square or rank-deficient systems, use Gauss-Jordan elimination.
It shows that each variable depends on ALL the coefficients and constants of the system, since each is a ratio of determinants involving the entire matrix. Keep this note short and outcome-focused for reuse.
Substitute each computed variable value back into every original equation. If LHS equals RHS for all equations, the solution is correct.