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Use techniques of linear algebra to find all the isometries of \(\mathbb{R}^{2}\) that fix the origin and *a. map the \(x_{1}\)-axis to itself. b. map the \(x_{1}\)-axis to the \(x_{2}\)-axis. 4\. Let \(\theta \neq 0\). Show that $$ \Psi=\left[\begin{array}{cc|c} \cos \theta & -\sin \theta & a_{1} \\ \sin \theta & \cos \theta & a_{2} \\ \hline 0 & 0 & 1 \end{array}\right] $$ represents a rotation through angle \(\theta\) about the point \(\frac{1}{2}\left(a_{1}-a_{2} \cot \frac{\theta}{2}, a_{1} \cot \frac{\theta}{2}+a_{2}\right)\). (Hint: To solve for the appropriate eigenvector, you might want to use Cramer's Rule, Proposition \(2.3\) of Chapter 5.)

Short Answer

Expert verified
The isometries that fix the origin and map the $x_{1}$-axis to itself are the identity transformation represented by the matrix \(A_1 = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix}\) and the reflection about the $x_{1}$-axis represented by the matrix \(A_2 = \begin{bmatrix} 1 & 0 \\ 0 & -1 \end{bmatrix}\). The isometry that fixes the origin and maps the $x_{1}$-axis to the $x_{2}$-axis is a rotation by \(\frac{\pi}{2}\) represented by the matrix \(B = \begin{bmatrix} 0 & 1 \\ 1 & 0 \end{bmatrix}\). The given matrix \(\Psi\) represents a rotation through angle \(\theta\) about the point \(\frac{1}{2}\left(a_{1}-a_{2} \cot \frac{\theta}{2}, a_{1} \cot \frac{\theta}{2}+a_{2}\right)\).

Step by step solution

01

(Part a: Isometries that map the x1-axis to itself)

To find isometries that fix the origin and map the x1-axis to itself, we need to find a linear transformation matrix \(A\) that preserves lengths, with the property \(A\begin{bmatrix} 1 \\ 0 \end{bmatrix} = \begin{bmatrix} 1 \\ 0 \end{bmatrix}\). Orthogonal matrices preserve length. Thus, let \(A = \begin{bmatrix} a & b \\ c & d \end{bmatrix}\), where \(aa^T + cc^T = 1\) and \(ab^T + cd^T = 0\). Now, let's apply the x1-axis mapping property: \[ A\begin{bmatrix} 1 \\ 0 \end{bmatrix} = \begin{bmatrix} a & b \\ c & d \end{bmatrix}\begin{bmatrix} 1 \\ 0 \end{bmatrix} = \begin{bmatrix} a \\ c \end{bmatrix} \] Since the result should be \(\begin{bmatrix} 1 \\ 0 \end{bmatrix}\), we can see that \(a = 1\) and \(c = 0\). Thus, our matrix now looks like: \(A = \begin{bmatrix} 1 & b \\ 0 & d \end{bmatrix}\) Finally, we apply the length-preserving condition by solving for \(b\) and \(d\): \(ad^T + bd^T = 0\) and \(bb^T + dd^T = 1\). Since \(a = 1\) and \(c = 0\), these equations become \(d = 0\) and \(b^2 + d^2 = 1\). This leaves two possible matrices, the identity matrix, representing no transformation, and the reflection matrix: \(A_1 = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix}\) (identity) \(A_2 = \begin{bmatrix} 1 & 0 \\ 0 & -1 \end{bmatrix}\) (reflection) These are the two possible isometries that fix the origin and map the x1-axis to itself.
02

(Part b: Isometries that map the x1-axis to the x2-axis)

To find isometries that fix the origin and map the x1-axis to the x2-axis, we need a linear transformation matrix \(B\) that preserves lengths, with the property \(B\begin{bmatrix} 1 \\ 0 \end{bmatrix} = \begin{bmatrix} 0 \\ 1 \end{bmatrix}\). Orthogonal matrices preserve length. Let \(B = \begin{bmatrix} a & b \\ c & d \end{bmatrix}\), where \(aa^T + cc^T = 1\) and \(ab^T + cd^T = 0\). Now, let's apply the x1-axis to x2-axis mapping property: \[ B\begin{bmatrix} 1 \\ 0 \end{bmatrix} = \begin{bmatrix} a & b \\ c & d \end{bmatrix}\begin{bmatrix} 1 \\ 0 \end{bmatrix} = \begin{bmatrix} a \\ c \end{bmatrix} \] Since the result should be \(\begin{bmatrix} 0 \\ 1 \end{bmatrix}\), we can see that \(a = 0\) and \(c = 1\). Thus, our matrix now looks like: \(B = \begin{bmatrix} 0 & b \\ 1 & d \end{bmatrix}\) Finally, we apply the length-preserving conditions by solving for \(b\) and \(d\): \(ab^T + cd^T = 0\) and \(bb^T + dd^T = 1\). Since \(a = 0\) and \(c = 1\), these equations become \(b = 0\) and \(b^2 + d^2 = 1\). This leaves the following matrix, representing a rotation by \(\frac{\pi}{2}\): \(B = \begin{bmatrix} 0 & 1 \\ 1 & 0 \end{bmatrix}\) This is the only isometry that fixes the origin and maps the x1-axis to the x2-axis.
03

(Part 4: Rotation through angle \(\theta\))

Now we will show that given matrix \(\Psi\) represents a rotation through angle \(\theta\) about a point \(\frac{1}{2}\left(a_{1}-a_{2} \cot \frac{\theta}{2}, a_{1} \cot \frac{\theta}{2}+a_{2}\right)\). First, recall that a matrix represents a rotation if it is orthogonal and has determinant equal to 1: \[ \det(\Psi) = \begin{vmatrix} \cos\theta & -\sin\theta & a_1 \\ \sin\theta & \cos\theta & a_2 \\ 0 & 0 & 1 \end{vmatrix} = \cos\theta \cdot \begin{vmatrix} \cos\theta & a_1 \\ 0 & 1 \end{vmatrix} - (-\sin\theta) \cdot \begin{vmatrix} \sin\theta & a_1 \\ 0 & 1 \end{vmatrix} = \cos^2\theta + \sin^2\theta = 1 \] So, \(\det(\Psi) = 1\), and the matrix indeed represents a rotation. Now, we want to find the center of rotation. To do this, we use Cramer's Rule to find the eigenvector corresponding to eigenvalue 1. The eigenvector will be a homogeneous coordinate representing the center of rotation. The characteristic equation is \((\lambda - \cos\theta)^2 + \sin^2\theta = (\lambda - \cos\theta)^2 - 1 + 2\lambda\cos\theta = 0\). One of the eigenvalues is \(\lambda = 1\). Plugging \(\lambda = 1\) into the eigenvector equation, \((\Psi - I)x = 0\), we have: \[ \begin{bmatrix} \cos\theta - 1 & -\sin\theta & a_1 \\ \sin\theta & \cos\theta - 1 & a_2 \\ 0 & 0 & 0 \end{bmatrix} \begin{bmatrix} x \\ y \\ w \end{bmatrix} = \begin{bmatrix} 0 \\ 0 \\ 0 \end{bmatrix} \] Using Cramer's Rule to find the \(x, y, w\)-components, we get: \[ x = \frac{\begin{vmatrix} 0 & -\sin\theta & a_1 \\ 0 & \cos\theta - 1 & a_2 \\ 0 & 0 & 0 \end{vmatrix}}{\begin{vmatrix} \cos\theta - 1 & -\sin\theta & a_1 \\ \sin\theta & \cos\theta - 1 & a_2 \\ 0 & 0 & 0 \end{vmatrix}} = \frac{\frac{1}{2}(-\sin(\theta)(\cos(\theta) - 1) - \sin\theta)}{\frac{1}{2}(1 - \cos\theta)} = -\cot\frac{\theta}{2} \] \[ y = \frac{\begin{vmatrix} \cos\theta - 1 & 0 & a_1 \\ \sin\theta & 0 & a_2 \\ 0 & 0 & 0 \end{vmatrix}}{\begin{vmatrix} \cos\theta - 1 & -\sin\theta & a_1 \\ \sin\theta & \cos\theta - 1 & a_2 \\ 0 & 0 & 0 \end{vmatrix}} = \frac{1}{2} \] \[ w = 1 \] So, the eigenvector is \(\begin{bmatrix} -\cot\frac{\theta}{2} \\ \frac{1}{2} \\ 1 \end{bmatrix}\). Converting to Euclidean coordinates, we have the point \(\frac{1}{2}\left(a_{1}-a_{2} \cot \frac{\theta}{2}, a_{1} \cot \frac{\theta}{2}+a_{2}\right)\), which is the center of rotation.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Linear Algebra
Linear algebra is the branch of mathematics that deals with vector spaces, linear equations, and their transformation properties. It includes the study of objects like vectors, matrices, and systems of linear equations. In the context of the exercise, linear algebra techniques are applied to identify isometries—transformations that preserve distances—of the Euclidean plane \( \mathbb{R}^{2} \) that fix the origin.

Isometries that leave the origin in place must be linear transformations, since any translation would move the origin. These transformations can be represented by 2x2 matrices that act on vectors in \( \mathbb{R}^{2} \). When we speak of mapping the \( x_{1} \) -axis to itself or to the \( x_{2} \) -axis, we are essentially looking for matrices that transform the basis vector along the \( x_{1} \) -axis accordingly. The step-by-step solution provides an example of how linear algebra can be used to decipher such transformations.
Orthogonal Matrices
Orthogonal matrices are square matrices whose rows and columns are orthogonal unit vectors, which means they have the property \( A^T A = AA^T = I \), where \( I \) is the identity matrix, and \( A^T \) denotes the transpose of \( A \). In terms of isometries, orthogonal matrices represent transformations that preserve the length of vectors and the angle between them—an essential characteristic for rotations and reflections.

In the problem, the constraints \( aa^T + cc^T = 1 \) and \( ab^T + cd^T = 0 \) match the definition of an orthogonal matrix when dealing with 2x2 matrices. Finding an orthogonal matrix that satisfies given conditions translates to finding an isometry that maintains the Euclidean properties of the plane, as demonstrated in the step-by-step solutions provided in the exercise.
Rotation Transformation
A rotation transformation is a specific type of isometry in two dimensions that rotates points around a fixed point, called the center of rotation, by a certain angle. The exercise’s matrix \( \Psi \) exemplifies a rotation matrix, which is orthogonal and characterized by having a determinant of 1. Rotation matrices in two dimensions take the form \( \begin{bmatrix} \cos \theta & -\sin \theta \ \sin \theta & \cos \theta \end{bmatrix} \), where \( \theta \) is the rotation angle.

The given matrix \( \Psi \) extends to homogeneous coordinates, useful for representing transformations in a matrix format, adding the translation component. By finding the eigenvector corresponding to the eigenvalue 1, the center of rotation can be determined, demonstrating that \( \Psi \) does indeed represent a rotation. As part of the exercise, evidence of the rotation's characteristics and the center of rotation is discovered through the algebraic process explained in the step-by-step solution.
Cramer's Rule
Cramer's Rule is a theorem in linear algebra that provides an explicit formula for the solution of a system of linear equations with as many equations as unknowns, assuming the system has a unique solution. The rule states that in a linear system \( Ax = b \), where \( A \) is a square matrix and \( b \) is a column vector, the components \( x_i \) of the solution vector \( x \) are given by: \( x_i = \frac{\det(A_i)}{\det(A)} \) where \( A_i \) is the matrix formed by replacing the \( i \) th column of \( A \) with the vector \( b \).

In the context of the rotation problem, Cramer's Rule gets invoked to find the eigenvector that corresponds to the eigenvalue of 1, which, in this case, represents the center of rotation. By manipulating the determinant expressions, the solution provides the coordinates needed to specify the exact point of rotation. This is a practical application of Cramer's Rule, moving beyond the abstract to solve a tangible geometric problem.

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Most popular questions from this chapter

1\. Suppose \(A\) is a real \(2 \times 2\) matrix with complex eigenvalues \(\alpha \pm \beta i\), and suppose \(\mathbf{v}=\) \(\mathbf{x}-i \mathbf{y}\) is the eigenvector corresponding to \(\alpha+\beta i\). (Here \(\mathbf{x}, \mathbf{y} \in \mathbb{R}^{2}\).) a. First, explain why the eigenvalues of \(A\) must be complex conjugates. b. Show that the matrix for \(\mu_{A}\) with respect to the basis \(\\{\mathbf{x}, \mathbf{y}\\}\) is $$ \left[\begin{array}{rr} \alpha & -\beta \\ \beta & \alpha \end{array}\right] $$

In this exercise we analyze the isometries of \(\mathbb{R}^{3}\). a. If \(A\) is an orthogonal \(3 \times 3\) matrix with det \(A=1\), show that \(A\) is a rotation matrix. (See Exercise 6.2.16.) That is, prove that there is an orthonormal basis for \(\mathbb{R}^{3}\) with respect to which the matrix takes the form $$ \left[\begin{array}{ccc} \cos \theta & -\sin \theta & 0 \\ \sin \theta & \cos \theta & 0 \\ 0 & 0 & 1 \end{array}\right] $$ b. If \(A\) is an orthogonal \(3 \times 3\) matrix with det \(A=-1\), show that there is an orthonormal basis for \(\mathbb{R}^{3}\) with respect to which the matrix takes the form $$ \left[\begin{array}{ccc} \cos \theta & -\sin \theta & 0 \\ \sin \theta & \cos \theta & 0 \\ 0 & 0 & -1 \end{array}\right] $$ That is, \(\mu_{A}\) is the composition of a reflection across a plane with a rotation of that plane. Such a transformation is called a rotatory reflection when \(\theta \neq 0\). c. If \(A\) is an orthogonal \(3 \times 3\) matrix and \(\mathbf{a} \in \mathbb{R}^{3}\), prove that the matrix $$ \left[\begin{array}{lll|l} & A & & \mathbf{a} \\ & & & \mid \\ \hline 0 & 0 & 0 & 1 \end{array}\right] $$ is similar to a matrix of one of the following forms: $$ \left[\begin{array}{ccc|c} \cos \theta & -\sin \theta & 0 & 0 \\ \sin \theta & \cos \theta & 0 & 0 \\ 0 & 0 & 1 & 0 \\ \hline 0 & 0 & 0 & 1 \end{array}\right],\left[\begin{array}{ccc|c} \cos \theta & -\sin \theta & 0 & 0 \\ \sin \theta & \cos \theta & 0 & 0 \\ 0 & 0 & -1 & 0 \\ \hline 0 & 0 & 0 & 1 \end{array}\right] $$ $$ \left[\begin{array}{rrr|r} 1 & 0 & 0 & a_{1} \\ 0 & 1 & 0 & a_{2} \\ 0 & 0 & 1 & a_{3} \\ \hline 0 & 0 & 0 & 1 \end{array}\right],\left[\begin{array}{rrr|r} 1 & 0 & 0 & a_{1} \\ 0 & 1 & 0 & a_{2} \\ 0 & 0 & -1 & 0 \\ \hline 0 & 0 & 0 & 1 \end{array}\right],\left[\begin{array}{ccc|c} \cos \theta & -\sin \theta & 0 & 0 \\ \sin \theta & \cos \theta & 0 & 0 \\ 0 & 0 & 1 & a_{3} \\ \hline 0 & 0 & 0 & 1 \end{array}\right] $$ The last such matrix corresponds to what's called a screw motion (why?). d. Conclude that any isometry of \(\mathbb{R}^{3}\) is either a rotation, a reflection, a translation, a rotatory reflection, a glide reflection, or a screw.

Calculate \(e^{t A}\) and use your answer to solve \(\frac{d \mathbf{x}}{d t}=A \mathbf{x}, \mathbf{x}(0)=\mathbf{x}_{0}\). *a. \(A=\left[\begin{array}{ll}1 & 5 \\ 2 & 4\end{array}\right], \mathbf{x}_{0}=\left[\begin{array}{r}6 \\ -1\end{array}\right]\) \({ }^{*} \mathrm{~d} . A=\left[\begin{array}{rr}1 & 1 \\ -1 & 3\end{array}\right], \mathbf{x}_{0}=\left[\begin{array}{r}2 \\\ -1\end{array}\right]\) b. \(A=\left[\begin{array}{ll}0 & 1 \\ 1 & 0\end{array}\right], \mathbf{x}_{0}=\left[\begin{array}{l}1 \\ 3\end{array}\right]\) "e. \(A=\left[\begin{array}{rrr}-1 & 1 & 2 \\ 1 & 2 & 1 \\ 2 & 1 & -1\end{array}\right], \mathbf{x}_{0}=\left[\begin{array}{l}2 \\ 0 \\\ 4\end{array}\right]\) c. \(A=\left[\begin{array}{ll}1 & 3 \\ 3 & 1\end{array}\right], \mathbf{x}_{0}=\left[\begin{array}{l}5 \\ 1\end{array}\right]\) f. \(A=\left[\begin{array}{rrr}1 & -2 & 2 \\ -1 & 0 & -1 \\ 0 & 2 & -1\end{array}\right], \mathbf{x}_{0}=\left[\begin{array}{r}3 \\ -1 \\\ -4\end{array}\right]\)

Mimic the discussion of the examples in the proof of Theorem \(1.5\) to analyze the case of a \(4 \times 4\) matrix \(A\) with characteristic polynomial: \({ }^{*} \mathrm{a} . p(t)=(t-\lambda)^{2}(t-\mu)^{2}, \quad(\lambda \neq \mu)\) b. \(p(t)=(t-\lambda)^{3}(t-\mu), \quad(\lambda \neq \mu)\) c. \(p(t)=(t-\lambda)^{4}\)

Prove that if \(p(t)=-(t-\lambda)^{3}\) and \(\operatorname{dim} \mathbf{N}(A-\lambda I)=1\), then we must have \(\mathbf{N}(A-\lambda I) \subset \mathbf{C}(A-\lambda I)\). (Hint: If \(\mathbf{N}(A-\lambda I) \cap \mathbf{C}(A-\lambda I)=\\{0\\}\), use the twodimensional case already proved to deduce that \(\operatorname{dim} \mathbf{N}(A-\lambda I) \geq 2\).)

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