Basis of kernel and image of a linear transformation. (All worked out)

In summary, the student has worked out the basis for the kernel of L1 and image of L2, denoted as U1 and U2 respectively. They are seeking clarification on how to find the union and sum of these two sets. The expert responds by stating that the direct sum is spanned by the union of the two sets of bases, and the intersection is given by a set of linear combinations of the basis of L1 and L2.
  • #1
sid9221
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http://dl.dropbox.com/u/33103477/linear%20transformations.png

My solution(Ignore part (a), this part (b) only)

http://dl.dropbox.com/u/33103477/1.jpg
http://dl.dropbox.com/u/33103477/2.jpg

So I have worked out the basis and for the kernel of L1 and image of L2, so I have U1 and U2.

Is what I have so far correct, and how do I proceed to find the Union and Sum of the two.

PS: This is a past exam paper for which I preparing, so it's not like I'm just getting my coursework done here..
 
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  • #2
The (direct) sum is very simple, it is spanned by the union of the two sets of bases. The intersection is given by {x|x=L1*a=L2*b for some a, b}, i.e., x is both a linear combination of basis of L1 and a (different) linear combination of basis of L2.
 

What is the basis of the kernel of a linear transformation?

The basis of the kernel of a linear transformation is the set of all vectors that are mapped to zero by the transformation. In other words, it is the set of all inputs that produce an output of zero.

What is the image of a linear transformation?

The image of a linear transformation is the set of all outputs that are obtained by applying the transformation to the input vectors. In other words, it is the range of the transformation.

How is the basis of the kernel related to the nullity of a linear transformation?

The basis of the kernel is directly related to the nullity of a linear transformation. The nullity is the dimension of the kernel, which is equal to the number of vectors in the basis of the kernel.

Can the basis of the kernel and the image be the same?

No, the basis of the kernel and the image cannot be the same. The basis of the kernel is a set of vectors that map to zero, while the basis of the image is a set of non-zero vectors that are obtained by applying the transformation to the input vectors.

How can the basis of the kernel and the image be used to determine the rank of a linear transformation?

The rank of a linear transformation is equal to the dimension of the image, which is also equal to the number of linearly independent vectors in the basis of the image. The dimension of the image plus the dimension of the kernel is equal to the dimension of the input vector space, which is also known as the rank-nullity theorem.

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