Linear Algebra Kernel/Image/Rank

In summary, we are given matrices A and B with dimensions 8x11 and 11x9 respectively. The kernel of A has dimension 5 and the image of B has dimension 7. The subspace kernel(A) + image(B) has dimension 10, and we are asked to find the rank of AB. Using the Rank Nullity Theorem, we can determine that the dimension of the image of A is 6 and the dimension of the kernel of B is 2. Therefore, the rank of AB is equal to the rank of A minus 2, which is 4.
  • #1
Zarlucicil
13
2

Homework Statement



Suppose that A is an 8x11 matrix whose kernel is of dimension 5, and B is an 11x9 matrix whose image is of dimension 7. If the subspace kernel(A) + image(B) has dimension 10, what is the rank of AB?

Homework Equations



Rank Nullity Theorem: For an n x m matrix A, dim(ker(A)) + dim(img(A)) = m

The Attempt at a Solution



Ok, so we know the following about A and B.

dim[ker(A)] = 5 (given)
dim[img(A)] = 6 (by rank-nullity theorem)

dim[img(B)] = 7 (given)
dim[ker(B)] = 2 (by rank-nullity theorem)

AB will be an 8x9 matrix, so the dimension of the image (which is the rank) can't be more than 8. I suppose the kernel of A is contained in AB, but I don't know what to make of that or how to use that information.

For the subspace ker(A) + img(B), could it be that ker(A) and img(B) share two components because the dimension is 10, but dim[ker(A)] + dim[img(B)] = 12?

This seems like a simple enough problem, but I have problems utilizing the meaning of kernel and image.
 
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  • #2
I'm not sure what you mean by "subspace kernel(A) + image(B)". What kind of "addition" are you doing here? More important is [itex]kernal(A)\cap image(B)[/itex], the intersection of the two spaces. u is in the kernel of AB if and only if Bu is in that intersection.
 
  • #3
If by "+" you mean "direct add" then I think we get this: There are two components shared between ker A and img B (because the dimension of the direct add of the two subspaces is two less than the sums of the dimensions).

What we are looking for is the dimension of the image of A(Bv).

Then we have: dim(img B) = 7. But since there are two components of the image of B which are in the kernel of A, the image of A when the source vector is Bv will be reduced by two (since those components will add the 0 vector to the result). Hence rank(AB) = rank(A) - 2 = 4.
 
  • #4
Ahh, I see. Thanks a lot for the help! I'm sorry my reply is so belated.

I suppose I meant "direct add" when I said "kernel(A) + image(B)" but my weak command of mathematics at the moment prevented me from knowing any other type of "addition"- so I'm sorry for not specifying.

I never remember to think of matrix multiplication in terms of a transformation of some sort- it clearly helps when trying to ascertain the image or kernel of some matrix.
 

Related to Linear Algebra Kernel/Image/Rank

What is the definition of a linear algebra kernel?

A linear algebra kernel, also known as the null space, is the set of all vectors in a vector space that when multiplied by a given matrix result in a zero vector. In other words, it is the set of all solutions to the homogeneous equation Ax=0.

What is the image of a linear transformation?

The image of a linear transformation is the set of all possible outputs that can be obtained by applying the transformation to the vectors in the input vector space. In other words, it is the range of the transformation.

What is the rank of a matrix?

The rank of a matrix is the maximum number of linearly independent rows or columns in the matrix. It can also be defined as the dimension of the column space or the row space of the matrix.

How is the rank of a matrix related to its kernel?

The rank of a matrix is equal to the dimension of its input vector space minus the dimension of its kernel. In other words, the rank of a matrix is the number of linearly independent columns or rows, which is the same as the number of non-zero columns or rows in its reduced row echelon form.

What is the significance of the kernel and image in linear algebra?

The kernel and image of a linear transformation are important concepts in linear algebra because they provide information about the behavior of the transformation. The kernel represents the part of the input vector space that is "ignored" or "collapsed" by the transformation, while the image represents the possible outputs of the transformation. Additionally, the rank of a matrix, which is related to its kernel and image, is a key factor in determining the invertibility of a matrix and the solvability of systems of linear equations.

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