Matrix Representation of Linear Transformation

Oct 13, 2025 by recoveryshake.com 115 views

Introduction

In linear algebra, a linear transformation is a function between vector spaces that preserves the operations of vector addition and scalar multiplication. The matrix representation of a linear transformation is a powerful tool for understanding and working with these transformations. In this article, we will explore the matrix representation of a linear transformation polynomial, specifically for the transformation $T:\mathbb{P_2} \to \mathbb{P_2}$ .

What is a Linear Transformation?

A linear transformation is a function $T:V \to W$ between two vector spaces $V$ and $W$ that satisfies the following two properties:

Additivity: For any vectors $u,v \in V$ , we have $T(u+v) = T(u) + T(v)$ .
Homogeneity: For any scalar $c$ and vector $v \in V$ , we have $T(cv) = cT(v)$ .

Polynomial Vector Spaces

The vector space $\mathbb{P_2}$ consists of all polynomials of degree at most 2, i.e., polynomials of the form $p(x) = ax^2 + bx + c$ , where $a,b,c \in \mathbb{R}$ . The set of all such polynomials forms a vector space under the usual operations of polynomial addition and scalar multiplication.

Matrix Representation of Linear Transformation

Given a linear transformation $T:\mathbb{P_2} \to \mathbb{P_2}$ , we can represent it as a matrix $A$ such that for any polynomial $p(x) = ax^2 + bx + c$ , we have $T(p(x)) = Ap(x)$ . The matrix $A$ is called the matrix representation of the linear transformation $T$ .

Finding the Matrix Representation

To find the matrix representation of a linear transformation $T:\mathbb{P_2} \to \mathbb{P_2}$ , we need to determine the images of the standard basis vectors of $\mathbb{P_2}$ . The standard basis vectors of $\mathbb{P_2}$ are:

$e_0(x) = 1$
$e_1(x) = x$
$e_2(x) = x^2$

We need to find the images of these basis vectors under the linear transformation $T$ .

Example: Finding the Matrix Representation

Suppose we have a linear transformation $T:\mathbb{P_2} \to \mathbb{P_2}$ given by the matrix:

A = \begin{bmatrix} 2 & 1 & 0 \\ 0 & 3 & 1 \\ 0 & 0 & 4 \end{bmatrix}

We need to find the images of the standard basis vectors under this linear transformation.

Image of $e_0(x) = 1$

We have:

T(e_0(x)) = Ae_0(x) = \begin{bmatrix} 2 & 1 & 0 \\ 0 & 3 & 1 \\ 0 & 0 & 4 \end{bmatrix} \begin{bmatrix} 1 \\ 0 \\ 0 \end{bmatrix} = \begin{bmatrix} 2 \\ 0 \\ 0 \end{bmatrix}

So, the image of $e_0(x) = 1$ under the linear transformation $T$ is $2e_0(x) = 2$ .

Image of $e_1(x) = x$

We have:

T(e_1(x)) = Ae_1(x) = \begin{bmatrix} 2 & 1 & 0 \\ 0 & 3 & 1 \\ 0 & 0 & 4 \end{bmatrix} \begin{bmatrix} 0 \\ 1 \\ 0 \end{bmatrix} = \begin{bmatrix} 1 \\ 3 \\ 0 \end{bmatrix}

So, the image of $e_1(x) = x$ under the linear transformation $T$ is $e_1(x) + 3e_0(x) = x + 3$ .

Image of $e_2(x) = x^2$

We have:

T(e_2(x)) = Ae_2(x) = \begin{bmatrix} 2 & 1 & 0 \\ 0 & 3 & 1 \\ 0 & 0 & 4 \end{bmatrix} \begin{bmatrix} 0 \\ 0 \\ 1 \end{bmatrix} = \begin{bmatrix} 0 \\ 1 \\ 4 \end{bmatrix}

So, the image of $e_2(x) = x^2$ under the linear transformation $T$ is $e_2(x) + 4e_0(x) = x^2 + 4$ .

Matrix Representation of the Linear Transformation

We have found the images of the standard basis vectors under the linear transformation $T$ . We can now write the matrix representation of the linear transformation $T$ as:

A = \begin{bmatrix} 2 & 1 & 0 \\ 0 & 3 & 1 \\ 0 & 0 & 4 \end{bmatrix}

This matrix represents the linear transformation $T:\mathbb{P_2} \to \mathbb{P_2}$ .

Conclusion

In this article, we have explored the matrix representation of a linear transformation polynomial, specifically for the transformation $T:\mathbb{P_2} \to \mathbb{P_2}$ . We have seen how to find the matrix representation of a linear transformation by determining the images of the standard basis vectors under the transformation. We have also seen an example of finding the matrix representation of a linear transformation. The matrix representation of a linear transformation is a powerful tool for understanding and working with these transformations.

References

[1] Linear Algebra and Its Applications by Gilbert Strang
[2] Linear Transformations and Matrices by Michael Artin

Introduction

In our previous article, we explored the matrix representation of a linear transformation polynomial, specifically for the transformation $T:\mathbb{P_2} \to \mathbb{P_2}$ . In this article, we will answer some frequently asked questions about the matrix representation of linear transformations.

Q: What is the matrix representation of a linear transformation?

A: The matrix representation of a linear transformation is a matrix that represents the transformation. It is a powerful tool for understanding and working with linear transformations.

Q: How do I find the matrix representation of a linear transformation?

A: To find the matrix representation of a linear transformation, you need to determine the images of the standard basis vectors under the transformation. You can then use these images to construct the matrix representation of the transformation.

Q: What are the standard basis vectors?

A: The standard basis vectors are the vectors that form the basis of the vector space. For the vector space $\mathbb{P_2}$ , the standard basis vectors are:

$e_0(x) = 1$
$e_1(x) = x$
$e_2(x) = x^2$

Q: How do I find the images of the standard basis vectors under a linear transformation?

A: To find the images of the standard basis vectors under a linear transformation, you need to apply the transformation to each of the standard basis vectors. You can then use the resulting vectors to construct the matrix representation of the transformation.

Q: What is the relationship between the matrix representation of a linear transformation and the transformation itself?

A: The matrix representation of a linear transformation is a way of representing the transformation as a matrix. The matrix representation of a linear transformation can be used to perform the transformation on a vector.

Q: Can I use the matrix representation of a linear transformation to find the inverse of the transformation?

A: Yes, you can use the matrix representation of a linear transformation to find the inverse of the transformation. The inverse of a linear transformation is a transformation that, when applied to the output of the original transformation, returns the original input.

Q: How do I find the inverse of a linear transformation using its matrix representation?

A: To find the inverse of a linear transformation using its matrix representation, you need to find the inverse of the matrix representation of the transformation. The inverse of a matrix is a matrix that, when multiplied by the original matrix, returns the identity matrix.

Q: What is the identity matrix?

A: The identity matrix is a square matrix that has 1s on the main diagonal and 0s elsewhere. It is a special matrix that, when multiplied by any other matrix, returns the original matrix.

Q: Can I use the matrix representation of a linear transformation to find the kernel of the transformation?

A: Yes, you can use the matrix representation of a linear transformation to find the kernel of the transformation. The kernel of a linear transformation is the set of all vectors that are mapped to the zero vector by the transformation.

Q: How do I find the kernel of a linear transformation using its matrix representation?

A: To find the kernel of a linear transformation using its matrix representation, you need to find the null space of the matrix representation of the transformation. The null space of a matrix is the set of all vectors that are mapped to the zero vector by the matrix.

Conclusion

In this article, we have answered some frequently asked questions about the matrix representation of linear transformations. We have seen how to find the matrix representation of a linear transformation, how to use the matrix representation to find the inverse of the transformation, and how to use the matrix representation to find the kernel of the transformation.

References

[1] Linear Algebra and Its Applications by Gilbert Strang
[2] Linear Transformations and Matrices by Michael Artin