Linear Algebra: find the Kernel and Image

In summary, we are asked to find the Kernel and Image of a linear transformation \varphi from V to V, where V is a subspace of the space of smooth functions \Re\rightarrow\Re spanned by sin and cos. After considering an arbitrary vector f_1 \in V, we find that the Kernel of \varphi is equal to {0}. The Image of \varphi is not the whole subspace V, as the coefficients depend on each other. Rather, the Image is all g\in V such that g(x)=(\lambda_1+\lambda_2)cosx+(\lambda_1-\lambda_2)sinx, \lambda_1,\lambda_2\in \Re.
  • #1
Theorem.
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Homework Statement


(a)Find the Kernel and Image of each of the following linear transformations.
...
(iv)[tex]\varphi : V\rightarrow V[/tex] given [tex] \varphi(f)=f'+f[/tex] where V is the subspace of the space of smooth functions [tex]\Re\rightarrow\Re[/tex] spanned by sin and cos, and f' denotes the derivative.
...

Homework Equations


The Attempt at a Solution


first I consider an arbitrary vector [tex]f_1 \in V[/tex]. then [tex]f_1[/tex] has the following form:
[tex] f_1=\lambda_1 sinx + \lambda_2 cosx [/tex] with[tex] \lambda_1\lambda_2\in \Re[/tex]
Then
[tex]\varphi(f_1)=\lambda_1 cosx-\lambda_2 sinx + \lambda_1 sinx+\lambda_2 cosx[/tex]
[tex]=(\lambda_1+\lambda_2)cosx+(lambda_1-lambda_2)sinx[/tex] call (*)

By definition,
[tex]Ker(\varphi)={f\in V: \varphi (f)=0}[/tex]

but from the above, [tex]\varphi (f)=0 [/tex] iff [tex] (\lambda_1+\lambda_2)=0, (\lambda_1-\lambda_2)=0[/tex] since this only occurs when both lambdas are zero, then
[tex]Ker(\varphi)={0}[/tex]

Originally, by (*) i wanted to say that the image of phi was the whole subpsace V, but this isn't true since the coefficients depend on each other (lambda1-lambda 2 and lambda1+lambda2). Rather the image would be all [tex]g\in V[/tex] such that [tex]g(x)=(\lambda_1+\lambda_2)cosx+(\lambda_1-\lambda_2)sinx, \lambda_1,\lambda_2\in \Re[/tex]

Does this seem right? I am skeptical for some reason.

accordingly a basis for the image would be [tex]{cosx+sinx, cosx-sinx}[/tex]

Can someone check this for me and let me know if if my argument seems right?
 
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  • #2
forgive me for my poor latex skills : (
 

FAQ: Linear Algebra: find the Kernel and Image

What is the difference between the kernel and image in linear algebra?

The kernel and image are two fundamental concepts in linear algebra that are related to the transformation of vectors. The kernel is the set of all vectors that get mapped to the zero vector by a linear transformation, while the image is the set of all vectors that can be obtained by applying the transformation to a given vector. In other words, the kernel represents the inputs that result in no change, while the image represents the outputs of the transformation.

How do you find the kernel of a linear transformation?

To find the kernel of a linear transformation, we need to solve the system of equations represented by the transformation's matrix. This can be done by using techniques such as Gaussian elimination or row reduction. The solution to the system of equations will give us the set of all vectors that map to the zero vector, which is the kernel.

Can the kernel and image of a linear transformation be empty?

Yes, it is possible for the kernel and image to be empty. This means that there are no vectors that get mapped to the zero vector or that can be obtained by applying the transformation to a given vector. In this case, the transformation is said to be injective, meaning that it preserves the distinctness of vectors.

How do you determine the dimension of the kernel and image?

The dimension of the kernel can be found by counting the number of free variables in the system of equations representing the linear transformation. This is also known as the nullity of the transformation. On the other hand, the dimension of the image can be found by counting the number of pivot columns in the matrix representing the transformation. This is also known as the rank of the transformation. The rank-nullity theorem states that the sum of the rank and nullity is equal to the number of columns in the matrix.

How are the kernel and image related to the invertibility of a linear transformation?

The kernel and image are closely related to the invertibility of a linear transformation. A transformation is invertible if and only if its kernel contains only the zero vector. In this case, the image of the transformation will be equal to the entire vector space. On the other hand, if the kernel is non-empty, then the transformation is not invertible. Additionally, the dimension of the kernel and image can tell us if a transformation is invertible. If both dimensions are equal to the number of columns in the matrix, then the transformation is invertible.

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