Prove Isomorphism When Columns of C are Linearly Independent

In summary: Additionally, since the columns of C are linearly independent, there is a unique solution for each column, meaning that every element in the range of L can be mapped to from an element in the domain, making L onto. Therefore, L is an isomorphism.
  • #1
LosTacos
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Homework Statement



Let L:R->R be a linear operator with matrix C. Prove if the columns of C are linearly independent, then L is an isomorphism.


Homework Equations





The Attempt at a Solution



Assume the columns of C are linearly independent. Then, the homogenous equation Cx=0 is the trivial solution. Need to show L is both 1-1 and onto. Assume that L(c1)=L(c2). Need to show that c1=c2. Well, since the matrix is linearly independent and Cx=0 is trivial solution, then each column represents its own solution. Therefore, if L(c1)=L(c2), then it must be that c1=c2. Now need to show L is onto. So for every d, there exists a c such that L(c)=d. Since C is linearly independent, no vector can be expressed as a linear combination of others. So, for each d, there will be a c where L(c)=d. Therefore, L is an isomorphism.
 
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  • #2
I think this should be L:Rn->Rn?
Then, the homogenous equation Cx=0 is the trivial solution.
An equation is not the same as a solution. The equation has the trivial solution only.

Well, since the matrix is linearly independent and Cx=0 is trivial solution, then each column represents its own solution.
How do you mean that?
Hint: Use linearity.

Now need to show L is onto.
"is onto"?

Since C is linearly independent, no vector can be expressed as a linear combination of others.
No vector of your matrix, right.
So, for each d, there will be a c where L(c)=d.
That does not follow from the previous statement.
 
  • #3
Okay so the equation has the trivial solution. So that means that the only solution is the trivial one, which is represented by the columns themselves, therefore 1-1. And since C is linearly independent, no vector in the matrix can be expressed as a linear combination of otheres. Therefore, each output has an input where L maps to that.
 
  • #4
So that means that the only solution is the trivial one, which is represented by the columns themselves, therefore 1-1.
The columns have no relation to the trivial solution. The trivial solution is x=0.Consider the linear operator F:R->R2, F(x)=(x,0). It satisfies everything you list in your posts, but it is not surjective: there is no x such that F(x)=(1,1). As long as you do not use that L maps Rn on itself, the argument cannot work.
 
  • #5
so how do you go about showing it is surjective
 
  • #6
Because the columns of A are linearly independent, each column will have a unique solutions such that there will exist an x such that L(x)=b which implies Ax=B
 
  • #7
Injective linear operators from R^n to R^n are always surjective. This follows from a more general result about the dimensions of the image and the kernel.
 

Related to Prove Isomorphism When Columns of C are Linearly Independent

1. What is isomorphism in linear algebra?

Isomorphism in linear algebra refers to the relationship between two vector spaces that are structurally similar. It means that the two vector spaces have the same dimensions and the same operations can be performed on them.

2. How do you prove isomorphism when the columns of matrix C are linearly independent?

To prove isomorphism when the columns of matrix C are linearly independent, we need to show that the columns of matrix C span the entire vector space and are linearly independent. This can be done by showing that the determinant of matrix C is non-zero, which indicates that the columns are linearly independent and can form a basis for the vector space.

3. What is the importance of proving isomorphism in linear algebra?

Proving isomorphism is important in linear algebra because it allows us to establish a one-to-one correspondence between two vector spaces, which helps us to solve problems in one space by using techniques from the other space. It also helps in simplifying calculations and making connections between different mathematical concepts.

4. Can a matrix have linearly dependent columns and still be isomorphic?

No, a matrix cannot have linearly dependent columns and still be isomorphic. Isomorphism requires the columns of the matrix to be linearly independent, as this ensures that the vector space is spanned by a set of basis vectors. Linearly dependent columns mean that the basis vectors are not independent, which violates the definition of isomorphism.

5. What are some other ways to prove isomorphism besides checking for linear independence of columns?

Besides checking for the linear independence of columns, other ways to prove isomorphism include showing that the matrix is invertible, proving that the matrix preserves operations such as addition and scalar multiplication, and demonstrating that the matrix satisfies the properties of a linear transformation.

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