Calculateur de Déterminant avec Étapes : Solutions Détaillées

Direct Answer / AI Overview: A determinant calculator with steps displays the complete intermediate mathematical operations required to find a matrix determinant. The four core step-by-step methods are:

1. Laplace Cofactor Expansion: Expands sub-matrix minors ($M_{ij}$) multiplied by alternating signs $(-1)^{i+j}$.
2. Gaussian Row Reduction: Applies elementary row operations to transform the matrix into an upper triangular form $U$, where $\det(A) = (-1)^{\text{swaps}} \prod u_{ii}$.
3. LU Decomposition: Factorizes $PA = LU$ into lower and upper triangular components in $O(n^3)$ time.
4. Sarrus’ Rule: Visual diagonal multiplication method exclusive to $3\times 3$ matrices.

When learning linear algebra or checking homework, getting only a single numeric scalar from a calculator is not enough. You need the complete step-by-step working showing minor expansions, intermediate row transformations, and sign adjustments.

Our free online Determinant Solver calculates determinants for matrices from $2\times 2$ up to $6\times 6$ with instantaneous results. In this guide, we break down each major computational method step-by-step so you can master both manual exams and computer implementations.

The 4 Step-by-Step Determinant Algorithms

                          ┌────────────────────────┐
                          │ Determinant Algorithms │
                          └───────────┬────────────┘
         ┌──────────────────┬─────────┴─────────┬──────────────────┐
         │                  │                   │                  │
┌────────┴─────────┐ ┌──────┴──────────┐ ┌──────┴──────────┐ ┌─────┴──────────┐
│ Laplace Minors   │ │ Row Reduction   │ │ LU Factorization│ │ Sarrus (3x3)   │
│ O(n!) - Symbolic │ │ O(n³) - Manual  │ │ O(n³) - Software│ │ O(1) - Fast    │
└──────────────────┘ └─────────────────┘ └─────────────────┘ └────────────────┘

Method 1: Laplace Cofactor Expansion (Step-by-Step)

Laplace expansion expresses the determinant as a weighted sum of smaller $(n-1)\times(n-1)$ sub-determinants along any row or column.

Mathematical Formula:

$$\det(A) = \sum_{j=1}^{n} a_{ij} C_{ij} = \sum_{j=1}^{n} a_{ij} (-1)^{i+j} \det(M_{ij})$$

Step-by-Step Process:

1. Choose the Best Row/Column: Select the row or column containing the greatest number of zeros to eliminate sub-calculations.
2. Determine the Sign Matrix: Apply the checkerboard sign pattern $(-1)^{i+j}$: $$\begin{bmatrix} + & - & + & - \ - & + & - & + \ + & - & + & - \ - & + & - & + \end{bmatrix}$$
3. Extract Minors ($M_{ij}$): For each entry, cross out its corresponding row and column to form the smaller sub-matrix.
4. Calculate Minor Determinants: Evaluate each $(n-1)\times(n-1)$ determinant.
5. Multiply and Sum: Multiply each entry by its sign and minor, then add them together.

Method 2: Gaussian Row Reduction (Upper Triangular Form)

For matrices larger than $3\times 3$, Laplace expansion requires too many operations ($n!$). Gaussian row reduction reduces the matrix to Upper Triangular form where all entries below the main diagonal are zero.

The Key Theorem:

The determinant of an upper or lower triangular matrix is the product of its diagonal entries: $$\det(U) = u_{11} \cdot u_{22} \cdot u_{33} \cdots u_{nn}$$

Step-by-Step Row Operation Rules:

1. Row Swap ($R_i \leftrightarrow R_j$): Multiplies determinant by $-1$.
2. Row Scaling ($k R_i \to R_i$): Multiplies determinant by $k$.
3. Row Addition ($R_i + c R_j \to R_i$): Leaves determinant completely unchanged.

Step-by-Step Worked Example:

Find $\det(A)$ using row reduction:

$$A = \begin{bmatrix} 2 & 4 & 2 \ 1 & 5 & 3 \ 4 & 1 & 2 \end{bmatrix}$$

• Step 1: Create zero in row 2 ($R_2 \to R_2 - 0.5 R_1$): $$R_2 = [1, 5, 3] - 0.5[2, 4, 2] = [0, 3, 2]$$
• Step 2: Create zero in row 3 ($R_3 \to R_3 - 2 R_1$): $$R_3 = [4, 1, 2] - 2[2, 4, 2] = [0, -7, -2]$$ $$A’ = \begin{bmatrix} 2 & 4 & 2 \ 0 & 3 & 2 \ 0 & -7 & -2 \end{bmatrix}$$
• Step 3: Eliminate entry in row 3, column 2 ($R_3 \to R_3 + \frac{7}{3} R_2$): $$R_3 = [0, -7, -2] + \frac{7}{3}[0, 3, 2] = \left[0, 0, -2 + \frac{14}{3}\right] = \left[0, 0, \frac{8}{3}\right]$$ $$U = \begin{bmatrix} 2 & 4 & 2 \ 0 & 3 & 2 \ 0 & 0 & \frac{8}{3} \end{bmatrix}$$
• Step 4: Multiply diagonal elements: $$\det(A) = 2 \times 3 \times \frac{8}{3} = 16$$

Method 3: LU Decomposition with Partial Pivoting

Modern numerical linear algebra engines—including Determinant Solver—use LU Decomposition with Partial Pivoting ($PA = LU$).

[ P ] · [ A ] = [ L ] · [ U ]

• $P$: Permutation matrix recording row swaps (with $\det(P) = (-1)^S$, where $S$ is swap count).
• $L$: Unit lower triangular matrix with 1s on the diagonal ($\det(L) = 1$).
• $U$: Upper triangular matrix.

$$\det(A) = (-1)^S \cdot \prod_{i=1}^{n} u_{ii}$$

This approach avoids division by near-zero pivots, ensuring floating-point accuracy up to 12 decimal places. Learn the deep math in our LU Decomposition Determinant Guide.

Algorithm Performance Comparison

Frequently Asked Questions (FAQ)

How can I calculate a determinant step-by-step for free?

You can use our online determinant calculator with steps to enter any matrix from $2\times 2$ to $6\times 6$. It computes determinants using LU decomposition with customizable precision and provides clear intermediate steps.

Why is row reduction faster than cofactor expansion?

Cofactor expansion has a factorial time complexity $O(n!)$, meaning a $5\times 5$ matrix requires evaluating 120 separate products. Gaussian row reduction operates in polynomial time $O(n^3) \approx 125$ basic arithmetic operations, making it thousands of times faster for larger matrices.

Does swapping rows change the determinant?

Yes. Every time you swap two rows in a matrix, the determinant is multiplied by $-1$. If you make an even number of swaps, the overall sign remains unchanged.