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19. Cramer’s Rule, Volume, and Linear Transformations

Dated: 23-04-2025

Cramer’s Rule

Let \(A\) be an invertible1 \(n \times n\) matrix2 such that for any \(\vec b \in \mathbb R^n\), the unique solution of \(x\) of \(A \vec x = \vec b\) has entries given by

\[x_i = \frac {\det (A_i(b))}{\det (A)}, \quad i = 1, 2, \ldots, n\]
What is \(A_i(b)\)

It means a matrix2 \(A^\prime\) obtained by replacing the \(i^{th}\) column of \(A\) with \(\vec b\).

Example

\[ \begin{align*} 3x_1 - 2x_2 &= 6 \\ -5x_1 + 4x_2 &= 8 \end{align*} \]

Solution

Re-writing in the matrix2 form \(A \vec x = \vec b\).

\[ \begin{bmatrix} 3 & -2 \\ -5 & 4 \end{bmatrix} \begin{bmatrix} x_1 \\ x_2 \end{bmatrix} = \begin{bmatrix} 6 \\ 8 \end{bmatrix} \]
\[ A = \begin{bmatrix} 3 & -2 \\ -5 & 4 \end{bmatrix}, x = \begin{bmatrix} x_1 \\ x_2 \end{bmatrix}, b = \begin{bmatrix} 6 \\ 8 \end{bmatrix} \]
\[ \det A = \begin{bmatrix} 3 & -2 \\ -5 & 4 \end{bmatrix} = 12 - 10 = 2 \]
\[ A_1(b) = \begin{bmatrix} 6 & -2 \\ 8 & 4 \end{bmatrix}, A_2(b) = \begin{bmatrix} 3 & 6 \\ -5 & 8 \end{bmatrix} \]
\[ x_1 = \frac{\det A_1(b)}{\det A} = \frac{24 + 16}{2} = \frac{40}{2} = 20 \]
\[x_2 = \frac{\det A_2(b)}{\det A} = \frac{24 + 30}{2} = \frac{54}{2} = 27\]

Theorem

Let \(A\) be an invertible1 \(n \times n\) matrix2 such that

\[A^{-1} = \frac {adj \, A}{\det (A)}\]
What is \(adj \, A\)

It is called classical adjoint or adjugate.

\[ adj \, A = \begin{bmatrix} C_{11} & C_{21} & \cdots & C_{n1} \\ C_{12} & C_{22} & \cdots & C_{n2} \\ \vdots & \vdots & \ddots & \vdots \\ C_{1n} & C_{2n} & \cdots & C_{nn} \end{bmatrix} \]

Where \(C_{ij}\) is the cofactor2 of \(a_{ij}\) entry2 of \(A\).

Determinants as Area or Volume

Theorem

  • For \(A_{2 \times 2}\), the area of a parallelogram is given by the columns of \(A\) such that \(|\det (A)|\).
  • For \(A_{3 \times 3}\), the area of a parallelepiped is given by the columns of \(A\) such that \(|\det (A)|\).

Example

Calculate the area of the parallelogram determined by the points \((-2, -2), (0, 3), (4, -1)\) and \((6, 4)\).

Solution

Let

  • \(A(-2, -2)\)
  • \(B(0, 3)\)
  • \(C(4, -1)\)
  • \(D(6, 4)\)

Let's fix \(A\) and then find adjacent side lengths of the parallelogram.

\[ AB = \begin{bmatrix} 0 - (-2) \\ 3 - (-2) \end{bmatrix} = \begin{bmatrix} 2 \\ 5 \end{bmatrix}, \quad AC = \begin{bmatrix} 4 - (-2) \\ -1 - (-2) \end{bmatrix} = \begin{bmatrix} 6 \\ 1 \end{bmatrix} \]
\[ = \left| \det \begin{bmatrix} 2 & 6 \\ 5 & 1 \end{bmatrix} \right| = |2 - 30| = |-28| = 28 \]

Linear Transformations

Theorem

If \(T : \mathbb R^2 \to \mathbb R^2\) be a linear transformation3 determined by a \(2 \times 2\) matrix2 \(A\) and \(S\) is a parallelogram in \(\mathbb R^2\) then.

\[\text{area of }T(S) = |\det (A)| \cdot \text{area of } S\]

Same goes for 3 dimensions.

Example

Let \(a\) and \(b\) be positive numbers.
Find the area of the region \(E\) bounded by an ellipse whose equation is

\[ \frac{x_1^2}{a^2} + \frac{x_2^2}{b^2} = 1 \]

Solution

We are claiming that \(E\) is the image of a disk \(D\) under the linear transformation3 \(A: D \to E\) determined by the matrix2

\[ A = \begin{bmatrix} a & 0 \\ 0 & b \end{bmatrix} \]
\[ A\vec u = \vec x \quad \text{ where } \vec u = \begin{bmatrix} u_1 \\ u_2 \end{bmatrix} \in D, \quad \vec x = \begin{bmatrix} x_1 \\ x_2 \end{bmatrix} \in E \]
\[ \implies \begin{bmatrix} a & 0 \\ 0 & b \end{bmatrix} \begin{bmatrix} u_1 \\ u_2 \end{bmatrix} = \begin{bmatrix} x_1 \\ x_2 \end{bmatrix} \]
\[ \implies \begin{bmatrix} au_1 \\ bu_2 \end{bmatrix} = \begin{bmatrix} x_1 \\ x_2 \end{bmatrix} \]
\[ \implies au_1 = x_1 \text{ and } bu_2 = x_2 \]
\[ \implies u_1 = \frac{x_1}{a} \text{ and } u_2 = \frac{x_2}{b} \]
\[\vec u \in D \implies |\vec u| \le 1\]
\[ (u_1^2 - 0) + (u_2^2 - 0) \leq 1 \]
\[ \implies \left(\frac{x_1}{a}\right)^2 + \left(\frac{x_2}{b}\right)^2 \leq 1 \quad \because u_1 = \frac{x_1}{a}, u_2 = \frac{x_2}{b} \]
\[\text{area of ellipse} = \text{area of } A(D)\]
\[= |\det(A)| \cdot \text{area of } D\]
\[ab \cdot \pi(1)^2 = \pi ab\]

References

Read more about notations and symbols.


  1. Read more about invertible matrices

  2. Read more about matrices

  3. Read more about linear transformations