Finding properties of a linear transformation

In summary, the linear transformation T(A)=Av, where v= (1, 2) and A is any 2×2 matrix, has a domain of the vector space R2x2, a target space of R2, a nullspace spanned by the vectors (-2, 0) and (0, -0.5), and an image that can be found by solving the equation A(1, 2) = (y1, y2). The rank and nullity can be determined by using the rank-nullity theorem.
  • #1
papaross
2
0

Homework Statement


Find the domain, target space, image, rank and nullity of the linear transformation T(A)=Av, where v= (1, 2) and A is any 2×2matrix.

Homework Equations



The Attempt at a Solution


I believe I know the domain (R2x2 vector space) and target space (R2), but I am not sure how to solve for the image, rank, and nullity. I have tried making the matrix A an arbitrary 2x2 matrix with the variables a, b, c, d but this doesn't seem to be making it easy to find the image or rank. And I think if I am able to find the rank, I can find the nullity based on the rank-nullity theorem.
 
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  • #2
papaross said:

Homework Statement


Find the domain, target space, image, rank and nullity of the linear transformation T(A)=Av, where v= (1, 2) and A is any 2×2matrix.

Homework Equations



The Attempt at a Solution


I believe I know the domain (R2x2 vector space)
Not sure what you mean by "R2x2 vector space".
papaross said:
and target space (R2), but I am not sure how to solve for the image, rank, and nullity. I have tried making the matrix A an arbitrary 2x2 matrix with the variables a, b, c, d but this doesn't seem to be making it easy to find the image or rank. And I think if I am able to find the rank, I can find the nullity based on the rank-nullity theorem.
What does the vector Av look like, using the matrix you described?

The nullspace is easy: Solve the equation
$$A\begin{bmatrix} 1 \\ 2 \end{bmatrix} = \begin{bmatrix} 0 \\ 0\end{bmatrix}$$

Finding the image is similar:
Solve the equation
$$A\begin{bmatrix} 1 \\ 2 \end{bmatrix} = \begin{bmatrix} y_1 \\ y_2\end{bmatrix}$$
 
  • #3
Mark44 said:
Not sure what you mean by "R2x2 vector space".

What does the vector Av look like, using the matrix you described?

The nullspace is easy: Solve the equation
$$A\begin{bmatrix} 1 \\ 2 \end{bmatrix} = \begin{bmatrix} 0 \\ 0\end{bmatrix}$$

Finding the image is similar:
Solve the equation
$$A\begin{bmatrix} 1 \\ 2 \end{bmatrix} = \begin{bmatrix} y_1 \\ y_2\end{bmatrix}$$
Ok, so if I let
A= a b
c d
then, solving for the nullspace, I would get something like $$t\begin{bmatrix} -2 \\ 0 \end{bmatrix} + s\begin{bmatrix} 0 \\ -0.5\end{bmatrix}$$, meaning $$\begin{bmatrix} -2 \\ 0 \end{bmatrix}, \begin{bmatrix} 0 \\ -0.5\end{bmatrix}$$ spans the nullspace?

and solving for the image would be similar of course.
 

1. What is a linear transformation?

A linear transformation is a mathematical function that maps points from one vector space to another in a way that preserves the linear structure of the original space.

2. How do you find the properties of a linear transformation?

The properties of a linear transformation can be found by evaluating the transformation on a set of basis vectors and observing how they are transformed. This can reveal properties such as linearity, invertibility, and dimension-preserving.

3. What are the key properties of a linear transformation?

The key properties of a linear transformation include linearity, which means that the transformation preserves addition and scalar multiplication, and invertibility, which means that the transformation has an inverse function.

4. Why is it important to find the properties of a linear transformation?

Understanding the properties of a linear transformation is crucial in many areas of mathematics and science, including linear algebra, differential equations, and physics. It allows us to analyze and solve problems involving transformations of vectors and matrices.

5. How can you use the properties of a linear transformation to solve problems?

Knowing the properties of a linear transformation can help in solving problems by providing a framework for understanding how vectors and matrices are transformed. This can be applied to a wide range of problems, such as finding solutions to systems of linear equations or optimizing functions.

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