Chapter 7: Problem 17
Let \(T\) be the linear transformation which reflects vectors about the \(x\) axis. Find a matrix for \(T\) and then find its eigenvalues and eigenvectors.
Short Answer
Expert verified
The matrix for T is \[ \begin{pmatrix} 1 & 0 \ 0 & -1 \end{pmatrix} \]. Its eigenvalues are 1 and -1, with corresponding eigenvectors \( \begin{pmatrix} x \ 0 \end{pmatrix} \) and \( \begin{pmatrix} 0 \ y \end{pmatrix} \).
Step by step solution
01
Determine the Matrix for Reflection
A reflection about the x-axis in two-dimensional space is performed by changing the sign of the y-coordinate. The matrix for this transformation is \[ T = \begin{pmatrix} 1 & 0 \ 0 & -1 \end{pmatrix} \]
02
Find the Eigenvalues
To determine the eigenvalues, solve the characteristic equation \[ \det(T - \lambda I) = 0 \]First, compute \(T - \lambda I\):\[ T - \lambda I = \begin{pmatrix} 1 & 0 \ 0 & -1 \end{pmatrix} - \begin{pmatrix} \lambda & 0 \ 0 & \lambda \end{pmatrix} = \begin{pmatrix} 1 - \lambda & 0 \ 0 & -1 - \lambda \end{pmatrix} \]Next, find the determinant:\[ \det(T - \lambda I) = (1 - \lambda)(-1 - \lambda) = 0 \]The characteristic equation simplifies to \[ (1 - \lambda)(-1 - \lambda) = 0 \]From this, the eigenvalues are \( \lambda_1 = 1 \) and \( \lambda_2 = -1 \).
03
Find the Eigenvectors
To find the eigenvectors, solve the equation \[ (T - \lambda I) \mathbf{v} = 0 \]For \( \lambda_1 = 1 \): Solve\[ \begin{pmatrix} 1 - 1 & 0 \ 0 & -1 - 1 \end{pmatrix} \begin{pmatrix} x \ y \end{pmatrix} = \begin{pmatrix} 0 \ 0 \end{pmatrix} \]This results in\[ \begin{pmatrix} 0 & 0 \ 0 & -2 \end{pmatrix} \begin{pmatrix} x \ y \end{pmatrix} = \begin{pmatrix} 0 \ 0 \end{pmatrix} \]Here, x can be any real number while y must be 0, so the eigenvector for \( \lambda_1 = 1 \) is\[ \mathbf{v}_1 = \begin{pmatrix} x \ 0 \end{pmatrix} \]For \( \lambda_2 = -1 \): Solve\[ \begin{pmatrix} 1 + 1 & 0 \ 0 & -1 + 1 \end{pmatrix} \begin{pmatrix} x \ y \end{pmatrix} = \begin{pmatrix} 0 \ 0 \end{pmatrix} \]This results in\[ \begin{pmatrix} 2 & 0 \ 0 & 0 \end{pmatrix} \begin{pmatrix} x \ y \end{pmatrix} = \begin{pmatrix} 0 \ 0 \end{pmatrix} \]Here, x must be 0 while y can be any real number, so the eigenvector for \( \lambda_2 = -1 \) is\[ \mathbf{v}_2 = \begin{pmatrix} 0 \ y \end{pmatrix} \]
Unlock Step-by-Step Solutions & Ace Your Exams!
-
Full Textbook Solutions
Get detailed explanations and key concepts
-
Unlimited Al creation
Al flashcards, explanations, exams and more...
-
Ads-free access
To over 500 millions flashcards
-
Money-back guarantee
We refund you if you fail your exam.
Over 30 million students worldwide already upgrade their learning with 91Ó°ÊÓ!
Key Concepts
These are the key concepts you need to understand to accurately answer the question.
Eigenvalues
Eigenvalues are special numbers associated with a matrix or a linear transformation like a reflection. They provide important insights into the behavior of the transformation. When you multiply a matrix by one of its eigenvectors, the result is a scaled version of that vector. The scaling factor is the eigenvalue. In our case, we have the linear transformation matrix for reflection about the x-axis given by \[ T = \begin{pmatrix} 1 & 0 \ 0 & -1 \end{pmatrix} \]. The eigenvalues for this matrix are found by solving the characteristic equation, which here are \( \lambda = 1 \) and \( \lambda = -1 \). These eigenvalues indicate that the transformation includes both stretching and reflection along different directions.
Eigenvectors
Eigenvectors are special vectors that only change in magnitude, not direction, when a linear transformation is applied to them. For our reflection matrix, we found two eigenvectors corresponding to our eigenvalues. For \( \lambda = 1 \), the eigenvector can be any vector of the form \( \mathbf{v}_1 = \begin{pmatrix} x \ 0 \end{pmatrix} \). This tells us that vectors along the x-axis remain in the same direction after reflection. For \( \lambda = -1 \), the eigenvector is any vector of the form \( \mathbf{v}_2 = \begin{pmatrix} 0 \ y \end{pmatrix} \). This means that vectors along the y-axis get inverted. Thus, eigenvectors reveal which directions are preserved and which are inverted by a transformation.
Characteristic Equation
The characteristic equation is a vital tool in finding the eigenvalues of a matrix. It is derived from the determinant of \( T - \lambda I \). For our reflection matrix, the characteristic equation is found as follows:\[ \text{det}(T - \lambda I) = (1 - \lambda)(-1 - \lambda) = 0 \]. This simplifies to\[ \lambda^2 - 1 = 0 \],which factors to\[ (\lambda - 1)(\lambda + 1) = 0 \].Solving this, we get the eigenvalues \( \lambda = 1 \) and \( \lambda = -1 \). The characteristic equation essentially helps us to identify those special numbers (eigenvalues) that are key to understanding the behavior of the transformation.
Reflection Matrix
A reflection matrix is used to describe a reflection transformation in linear algebra. For a reflection about the x-axis, the transformation matrix is\[ T = \begin{pmatrix} 1 & 0 \ 0 & -1 \end{pmatrix} \].This matrix changes the sign of the y-coordinate while keeping the x-coordinate unchanged. When a vector is multiplied by this matrix, its y-coordinate is flipped.
Example:
If \( \mathbf{v} = \begin{pmatrix} x \ y \end{pmatrix} \) then \( T \mathbf{v} = \begin{pmatrix} x \ -y \end{pmatrix} \).This simple reflection provides a rich example to understand transformations, eigenvalues, and eigenvectors in linear algebra, showing how such a straightforward operation can elegantly illustrate fundamental concepts.
Example:
If \( \mathbf{v} = \begin{pmatrix} x \ y \end{pmatrix} \) then \( T \mathbf{v} = \begin{pmatrix} x \ -y \end{pmatrix} \).This simple reflection provides a rich example to understand transformations, eigenvalues, and eigenvectors in linear algebra, showing how such a straightforward operation can elegantly illustrate fundamental concepts.