Proving KerD^2=KerD and ImD=ImD^2 with Linear Transformations

In summary, Hurkyl was wrong and it would be simpler to say that dimA=dimA and hence the two structures are isomorphic which means that KerD={0} and ImD=A.
  • #1
ergonomics
14
0
If i am given a linear transformation D:A->A,that is followed by
A=ImD(+)kerD
and i am asked to prove that kerD^2=kerD and imD=imD^2.

instead of trying to work it out the hard way by showing that every element of KerD is an element of kerD^2 , both directions.

would it not be easier to just say that dimA=dimA and hence the two structures are isomorphic which means that KerD={0} and ImD=A.

same goes for D^2:A->A
KerD^2={0}
ImD^2=A

=> therefore KerD^2=KerD and ImD^2=ImD ?
 
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  • #2
ergonomics said:
would it not be easier to just say that dimA=dimA and hence the two structures are isomorphic which means that KerD={0} and ImD=A.
Why does it mean that?
 
  • #3
would it not be easier to just say that dimA=dimA and hence the two structures are isomorphic
What two structures?
which means that KerD={0} and ImD=A.
Huh? I'm not sure what in the world you're doing, but just because A is isomorphic to A doesn't mean that every linear map D:A->A is an isomorphism. Is that what you were thinking?
 
  • #4
According to my book two vector spaces of the same dimension are isomorphic to each other.
and the proof also apparently seems to be pretty simple.
If B is a basis for A, then we can easily show that ImD=A
and that T is injective and if T is injective then KerD={0}
 
  • #5
If B is a basis for A, then we can easily show that ImD=A
and that T is injective and if T is injective then KerD={0}
You're forgetting one of the hypotheses for the theorem -- you need D to be an isomorphism.
 
  • #6
yes akg unfortunately that is what i was thinking, that if the two were isomorphic to each other, then the map would necessairly be an isormophism.

just a few minutes before hurkyl put his post up, i was about to say that i went over the theorems in my book again, and that my line of thought was incorrect.

anyway, thank you all.
 
Last edited:

Related to Proving KerD^2=KerD and ImD=ImD^2 with Linear Transformations

1. What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another in a way that preserves the basic structure of the space. This means that the transformation preserves straight lines, the origin, and the relative distance and direction between points.

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

The key properties of a linear transformation are preservation of addition and scalar multiplication. This means that the transformation must satisfy the following equations for any vectors v and w and any scalar c: T(v + w) = T(v) + T(w) and T(cv) = cT(v). In other words, the transformation must preserve vector addition and scaling.

3. How can a linear transformation be represented?

A linear transformation can be represented using a matrix. The input vector is multiplied by the transformation matrix to produce the output vector. The size of the matrix will depend on the dimensions of the input and output vector spaces.

4. What is the relationship between linear transformations and systems of linear equations?

Linear transformations and systems of linear equations are closely related. A system of linear equations can be represented as a matrix equation, where the coefficients of the variables are the entries of the matrix. Solving the system is equivalent to finding the vector that is mapped to the zero vector by the linear transformation represented by the matrix.

5. Can a linear transformation be invertible?

Not all linear transformations are invertible. For a linear transformation to be invertible, it must satisfy certain conditions, such as having a unique solution for each output vector. If a linear transformation is invertible, then its inverse will also be a linear transformation.

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