Linear algebra and column spaces

In summary, the conversation discusses finding the image of a linear transformation T associated with a matrix A. It is mentioned that the image of T is given by the column space of A. The conversation then goes on to discuss finding a vector B that is part of the image of T. The method suggested is to write the matrix (A|B) and find its columnspace. However, it is clarified that this approach adds B to the image by defining a new linear transformation T'. Finally, it is confirmed that this is the desired outcome and the conversation ends with gratitude and congratulations.
  • #1
Niles
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Homework Statement


I have a linear transformation T which is associated with a matrix A. I want to find the image of T, which is given by the column space of A. I have done this.

Now I have a vector B = (x,y,z,w)^T, and I want the vector B to be part of the image of T. To do this, I write the matrix P, which is given by (A|B). Do I just set this matrix equal zero and solve it to find the image of T, wherein the matrix B is?
 
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  • #2
That sounds to me like you would be solving the equation AB= 0- which would give the kernel of T, not its image.

I'm not clear on what your question is. you say you have already found the image of T, now you want to find a vector B in the image of T? Any one of the columns of A corresponds to a vector that is in the image of T.
 
  • #3
I want to find the image of T, where the columnvector B is part of the image of T.

What I do is that I write (A|B), bring to rref and find the columnspace from that. What I wrote in my original post was a mistake. But from what you wrote ("Any one of the columns of A corresponds to a vector that is in the image of T"), I guess my method will work?
 
  • #4
Niles said:
I want to find the image of T, where the columnvector B is part of the image of T.

What I do is that I write (A|B), bring to rref and find the columnspace from that. What I wrote in my original post was a mistake. But from what you wrote ("Any one of the columns of A corresponds to a vector that is in the image of T"), I guess my method will work?

It will work if you realize what you are doing. You are adding B to the image by defining a new linear transformation T' corresponding to the matrix (A|B) with domain in a space one dimension higher than T. You can't 'add' anything to the range of T itself. Is that what you want to do?
 
  • #5
Yes, that is exactly what I want to do. Your explanation cleared it for me - thanks, and congratulations with your award.
 
  • #6
Thanks! And you're welcome!
 

FAQ: Linear algebra and column spaces

What is linear algebra?

Linear algebra is a branch of mathematics that deals with vector spaces and linear transformations between them. It involves the study of systems of linear equations and their solutions, as well as concepts such as matrices, determinants, and eigenvalues.

What is a column space?

A column space is the set of all possible linear combinations of the columns of a matrix. It represents the span of the columns and can also be thought of as the subspace of the vector space spanned by the columns of the matrix.

Why is column space important?

Column space is important because it is closely related to the rank of a matrix. The rank of a matrix is the maximum number of linearly independent columns, and it can be used to determine important properties of the matrix, such as invertibility and solutions to systems of linear equations.

How do you find the column space of a matrix?

The column space of a matrix can be found by performing row reduction on the matrix and looking at the non-zero rows in the reduced row echelon form. The columns corresponding to these non-zero rows form a basis for the column space.

What is the relationship between column space and null space?

The column space and null space of a matrix are complementary subspaces. This means that any vector in the null space is orthogonal (perpendicular) to any vector in the column space. Additionally, the dimensions of the column space and null space add up to the number of columns in the matrix.

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