How can we generalize this proof to infinite dimensional vector spaces?

  • Thread starter Maybe_Memorie
  • Start date
  • Tags
    Proof
In summary, the conversation discusses how to show that for any three linear operators A, B, and C in a vector space V, the rank of their product ABC is less than or equal to the rank of B. This is proven by using the dimension formula and showing that the image of ABC is a subset of the image of AB, which leads to the conclusion that rk(ABC) =< rk(AB). This proof assumes that V is finite dimensional.
  • #1
Maybe_Memorie
353
0

Homework Statement



Let V be a vector space. Show that for every three linear operators
A,B,C: V -> V, we have
rk(ABC) =< rk(B)

Homework Equations



V = rk(A) + dimKer(A)

rk(A) = dimIm(A)

The Attempt at a Solution



Im(ABC) = {ABC(v) | vEV}
= {AB(C(v)) | vEV}

So Im(ABC) is a subset of Im(AB)

So dimIm(ABC) =< dimIm(AB)

So rk(ABC) =< rk(AB)

Im(AB) = {A(B(w)) | wEV}

Ker(B) subset of ker(AB) because if Bx=0, then ABx = A0 = 0

By the dimension formula, this leads to rk(B) >= rk(AB)

So putting the two results together we get rk(ABC) =< rk(B)


Is this correct? Thanks!
 
Physics news on Phys.org
  • #2
Hi Maybe_Memorie! :smile:

Your proof seems correct! But it seems you'll need V to be finite dimensional for it to work.
 
  • #3
micromass said:
Hi Maybe_Memorie! :smile:

Your proof seems correct! But it seems you'll need V to be finite dimensional for it to work.

We've only dealt with finite dimensional vector spaces.
 

Related to How can we generalize this proof to infinite dimensional vector spaces?

1. What is the Rank-Image-Kernel theorem?

The Rank-Image-Kernel theorem, also known as the Fundamental Theorem of Linear Algebra, states that for a linear transformation from vector space V to vector space W, the rank of the transformation (dimension of the image) plus the dimension of the kernel (nullity) is equal to the dimension of V. In other words, it describes the relationship between the dimensions of the input and output spaces of a linear transformation.

2. How is the Rank-Image-Kernel theorem used in linear algebra?

The Rank-Image-Kernel theorem is used to determine the dimension of the image and kernel of a linear transformation, which can in turn provide information about the properties and behavior of the transformation. It is also a key component in solving systems of linear equations, as it can help determine the number of solutions and the existence of a unique solution.

3. What is the difference between rank and image in the Rank-Image-Kernel theorem?

The rank of a linear transformation is the dimension of its image, or the number of independent columns in the transformation's matrix representation. The image, on the other hand, refers to the actual set of vectors in the output space of the transformation. In other words, the rank describes the size of the image, while the image itself describes the specific vectors that make up the image.

4. How does the Rank-Image-Kernel theorem relate to vector spaces?

The Rank-Image-Kernel theorem is a fundamental property of vector spaces, as it describes the relationship between the dimensions of the input and output spaces of a linear transformation. It also provides a way to determine the basis for the image and kernel of a transformation, which are important concepts in vector space theory.

5. Can the Rank-Image-Kernel theorem be extended to non-linear transformations?

No, the Rank-Image-Kernel theorem only applies to linear transformations. Non-linear transformations do not have a well-defined image and kernel, so the theorem cannot be extended to them. However, there are other theorems and concepts in mathematics that can be used to study the properties of non-linear transformations.

Similar threads

  • Calculus and Beyond Homework Help
Replies
14
Views
2K
  • Calculus and Beyond Homework Help
Replies
7
Views
3K
  • Calculus and Beyond Homework Help
Replies
1
Views
2K
  • Calculus and Beyond Homework Help
Replies
7
Views
444
  • Calculus and Beyond Homework Help
Replies
7
Views
1K
  • Calculus and Beyond Homework Help
Replies
0
Views
469
  • Calculus and Beyond Homework Help
Replies
1
Views
818
  • Calculus and Beyond Homework Help
Replies
1
Views
1K
  • Calculus and Beyond Homework Help
Replies
9
Views
1K
  • Calculus and Beyond Homework Help
Replies
9
Views
1K
Back
Top