Given two linear transformations L and K, show ##K = \lambda L## holds

In summary, we are given a real vector space ##V## of finite dimension ##n## and two linear transformations ##L,K:V \rightarrow \Re## such that ##ker(L) \subset ker(K)##. From this, we can deduce that there exists a parameter ##\lambda \in \Re## such that ##K=\lambda L##. We can show this by setting ##\lambda=0## when ##K=0## and by computing ##dim(kerK)## to be ##n-1## and ##dim(kerL)## to be ##n-1##, and thus showing that they are equal. In the case when ##K \neq 0##, we can
  • #1
JD_PM
1,131
158
TL;DR Summary
Exercise on linear transformations and dimensional theorem.
Let ##V## be a real vectorspace of finite dimension ##n##. Let ##L, K:V \rightarrow \Re## be linear transformations so that ##ker(L) \subset ker(K)##. Then there's a parameter ##\lambda \in \Re## so that ##K=\lambda L##

a) Show that ##K=\lambda L## holds when ##K=0##.

b) Suppose that ##K \neq 0##. Compute ##dim(kerK)## and show that ##dim(kerK)=dim(kerL)##.

c) Show that ##K=\lambda L## holds when ##K \neq 0##.a) Here I think we just have to set ##\lambda=0##and then the equation holds. However I am not that convinced (it seems too easy).

b) To compute ##dim(kerK)## I used the dimension theorem for the linear transformation ##K:V \rightarrow \Re##

$$dim(V) = dim(kerK) + dim(Imk)$$

We are given that ##dim(V)=n## and the co-domain of both linear transformations is ##\Re##. Thus I get

$$dim(kerK)=n-1$$

To compute ##dim(kerL)## I used the dimension theorem for the linear transformation ##L:V \rightarrow \Re##

$$dim(V) = dim(kerL) + dim(ImL)$$

We are given that ##dim(V)=n## and the co-domain of both linear transformations is ##\Re##. Thus I get

$$dim(kerL)=n-1$$

Then indeed we get $$dim(kerL)=dim(kerK)$$ Is this OK?

c) I do not really know how to approach this section. Could you please give a hint?
Any help is appreciated.

Thanks.
 
Physics news on Phys.org
  • #2
Let ##v## be a vector not in ##\text{Ker}(K)## and choose ##\lambda## so that ##K(v)=\lambda L(v).## You can do this because ##\text{Ker}(L)\subset\text{Kerl}(K)##, so ##L(v)\neq 0##. What can you say about the kernel of ##K-\lambda L##?
 
  • Like
Likes JD_PM
  • #3
Let ##X,Y,Z## be vector spaces perhaps infinite dimensional. And let ##A:X\to Y,\quad B:X\to Z## be linear operators; and ##A(X)=Y##.

THEOREM. Assume that ##\ker A\subseteq \ker B##. Then there exists a unique linear operator ##\Lambda:Y\to Z## such that ##B=\Lambda A##.

Indeed, For any element ##y\in Y## there is an element ##x\in X## such that ##Ax=y##. By definition put
##\Lambda y:=Bx##. This definition is correct: if there exists another ##x'\in X## such that ##Ax'=y## then ##x-x'\in\ker A\subseteq\ker B## thus ##Bx=Bx'##. qed
 
Last edited:
  • Like
Likes JD_PM
  • #4
wrobel said:
Let ##X,Y,Z## be vector spaces perhaps infinite dimensional. And let ##A:X\to Y,\quad B:X\to Z## be linear operators; and ##A(X)=Y##.

THEOREM. Assume that ##\ker A\subseteq \ker B##. Then there exists a unique linear operator ##\Lambda:Y\to Z## such that ##B=\Lambda A##.

Indeed, For any element ##y\in Y## there is an element ##x\in X## such that ##Ax=y##. By definition put
##\Lambda y:=Bx##. This definition is correct: if there exists another ##x'\in X## such that ##Ax'=y## then ##x-x'\in\ker A\subseteq\ker B## thus ##Bx=Bx'##. qed
I don't see where we assumed A is surjective. Besides, please don't provide a full solution, we want to lead the OP to find it by themselves.
 
  • Like
Likes JD_PM
  • #5
WWGD said:
I don't see where we assumed A is surjective
this assumption does not shrink generality: you can always take ##A(X)## on a role ##Y## and then extend ##\Lambda## from ##A(X)## to the whole space. But such an extension is not unique.

WWGD said:
Besides, please don't provide a full solution, we want to lead the OP to find it by themselves.
It is a very special situation. There is a classical theorem and I believe that it would be better if the OP would know this classical theorem and its regular proof instead of inventing strange proofs of special cases of this classical theorem.
 
  • #6
wrobel said:
this assumption does not shrink generality: you can always take ##A(X)## on a role ##Y## and then extend ##\Lambda## from ##A(X)## to the whole space. But such an extension is not unique.It is a very special situation. There is a classical theorem and I believe that it would be better if the OP would know this classical theorem and its regular proof instead of inventing strange proofs of special cases of this classical theorem.
Never mind, I realize in this case the map must be onto because the image is a vector space and R is 1-dimensional over itself.
 
  • #7
Infrared said:
What can you say about the kernel of ##K-\lambda L##?

Based on the definition and assuming that ##v## is not in ##ker(K-\lambda L)## I get

$$K(v) \neq \lambda L(v)$$

But could you please explain why this is relevant?
 
  • #8
I didn't assume ##v## is not in the kernel of ##K-\lambda L##. I assumed ##v## was not in the kernel of ##K##.
 
  • Like
Likes JD_PM
  • #9
By the way, the standard generalization of the fact from the top is as follows. Let ##f,f_1,\ldots,f_n:X\to\mathbb{R}## be linear functions such that ##\bigcap_{i=1}^n\ker f_i\subseteq\ker f.## Then there exist constants ##\lambda_1,\ldots,\lambda_n## such that ##f=\sum_{i=1}^n\lambda_i f_i##.

This fact follows from the above theorem. Indeed, let
$$Y=A(X)\subseteq\mathbb{R}^n,\quad Z=\mathbb{R},\quad B=f,\quad Ax=(f_1(x),\ldots,f_n(x))^T.$$
By the proved above theorem there exist a linear function ##\Lambda: A(X)\to\mathbb{R}## such that ##B=\Lambda A##. If needed, ##\Lambda## can be extended to the whole ##\mathbb{R}^n## and thus ##\Lambda A=(\lambda_1,\ldots,\lambda_n)(f_1,\ldots,f_n)^T##.
 
Last edited:
  • #10
work on V/ker(L). then you are reduced to showing two linear maps R-->R are scalar multiples of each other, which is trivial.
 
Last edited:
  • Like
Likes JD_PM

1. What is the definition of a linear transformation?

A linear transformation is a mathematical function that maps a vector space to itself, preserving the operations of addition and scalar multiplication. It can be represented by a matrix or a set of linear equations.

2. How do you prove that two linear transformations are equal?

To prove that two linear transformations are equal, you must show that they produce the same output for every input. This can be done by showing that they have the same matrix representation or by using the definition of a linear transformation to prove that they have the same effect on vectors.

3. What does the notation ##K = \lambda L## mean?

This notation means that the linear transformation K is equal to a scalar multiple (represented by lambda, ##\lambda##) of the linear transformation L. In other words, K is a scaled version of L.

4. How do you show that ##K = \lambda L## holds for two linear transformations?

To show that ##K = \lambda L## holds, you must demonstrate that K and L have the same matrix representation or that they have the same effect on vectors. This can be done by performing calculations with the matrices or by using the properties of linear transformations.

5. What is the significance of showing that ##K = \lambda L## holds for two linear transformations?

Showing that ##K = \lambda L## holds for two linear transformations means that they are equivalent in terms of their effects on vectors. This can be useful in simplifying calculations and understanding the relationship between different linear transformations.

Similar threads

  • Linear and Abstract Algebra
Replies
2
Views
997
  • Linear and Abstract Algebra
Replies
4
Views
989
  • Linear and Abstract Algebra
Replies
24
Views
3K
  • Calculus and Beyond Homework Help
Replies
14
Views
599
  • Linear and Abstract Algebra
Replies
20
Views
3K
  • Linear and Abstract Algebra
Replies
1
Views
840
  • Linear and Abstract Algebra
Replies
12
Views
1K
  • Linear and Abstract Algebra
Replies
1
Views
1K
  • Linear and Abstract Algebra
Replies
5
Views
1K
  • Linear and Abstract Algebra
Replies
10
Views
1K
Back
Top