A question about the rank of a linear operator

In summary, the conversation discusses a linear transformation T from a finite vector space V to itself. The rank of T is equivalent to the rank of TT, and it is questioned why the intersection of the image of T and the kernel of T is always the zero vector. The answer is explained by considering the special case where T is a projection, and it is concluded that if the kernel of T intersected the image, then T would not be a projection.
  • #1
sanctifier
58
0
Let T is a linear transformation from a vector space V to V itself. The dimension of V, denoted by dim(V), is finite.

If the rank of T, denoted by rk(T), is equivalent to the rank of TT, i.e., rk(T)=rk(TT)

why is the intersection of image of T(denoted by im(T)) and the kernel of T(denoted by ker(T)) is the zero vector, i.e., im(T)[tex]\cap[/tex]ker(T)={0}?

Thanks for any help.
 
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  • #2
Think first of the special case where T is a projection. Then the image of T and the image of TT are clearly the same. In such a case, if the kernel of T intersected the image, then T wouldn't be a projection.
 
  • #3
You are right.

Thanks!
 

1. What is the rank of a linear operator?

The rank of a linear operator is the number of linearly independent rows or columns in a matrix.

2. How is the rank of a linear operator calculated?

The rank of a linear operator can be calculated by finding the maximum number of linearly independent rows or columns in a matrix.

3. What does the rank of a linear operator tell us?

The rank of a linear operator is an important measure of its dimension and determines its properties such as invertibility and solutions to linear equations.

4. Can the rank of a linear operator change?

Yes, the rank of a linear operator can change if the matrix undergoes row or column operations such as multiplication or addition by a scalar or swapping rows or columns.

5. How is the rank of a linear operator related to its nullity?

The rank of a linear operator and its nullity (the number of linearly independent solutions to the equation Ax=0) are related by the rank-nullity theorem, which states that the sum of the rank and nullity is equal to the number of columns in the matrix.

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