What is the Dimension of the Intersection of Two Kernels in a Vector Space?

In summary, given transformations T_1, T_2:V->F where V is a vector space with dimension n over the field F, and N_1 = KerT_1, N_2 = KerT_2, and N_1 =/= N_2, the dimension of their intersection is n-2. This is determined by using the equation dim(A+B) = dimA + dimB - dim(A intersection B) and considering the fact that N_1 + N_2 is in V. However, this solution may not hold true in all cases, as the dimension of the image of an arbitrary non-trivial linear transformation is not always 1.
  • #1
daniel_i_l
Gold Member
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Homework Statement


Given transformations T_1, T_2:V->F where V is a vector space with the dimension n over the field F, T_1 , T_2 =/= 0. If N_1 = KerT_1 , N_2 = KerT_2 and N_1 =/= N_2 find dim(N_1 intersection N_2)


Homework Equations



dim(A+B) = dimA + dimB - dim(A intersection B)

The Attempt at a Solution


First of all, dimImT_1 = dimImT_2 = 1 so N_1 = N_2 = n-1.
Also, N_1 + N_2 in V so
dim(N_1 + N_2) = n-1+n-1-dim(N_1 intersection N_2)
<= n and so we get that dim(N_1 intersection N_2) >= n-2.
But since N_1 intersection N_2 in N_1 we get
n-1 >= dim(N_1 intersection N_2) >= n-2. But since N_1 =/= N_2 obviously
dim(N_1 intersection N_2) =/= n-1 and so dim(N_1 intersection N_2) =n-2.
Is that right? Did I leave out any important step?
Thanks.
 
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  • #2
daniel_i_l said:

Homework Statement


Given transformations T_1, T_2:V->F where V is a vector space with the dimension n over the field F, T_1 , T_2 =/= 0. If N_1 = KerT_1 , N_2 = KerT_2 and N_1 =/= N_2 find dim(N_1 intersection N_2)


Homework Equations



dim(A+B) = dimA + dimB - dim(A intersection B)

The Attempt at a Solution


First of all, dimImT_1 = dimImT_2 = 1 so N_1 = N_2 = n-1.
Why is that true? What if T_1 were the identity transformation? How do you conclude that the dimension of the image of an arbitrary non-trivial linear transformation is 1 no matter what n is?

Also, N_1 + N_2 in V so
dim(N_1 + N_2) = n-1+n-1-dim(N_1 intersection N_2)
<= n and so we get that dim(N_1 intersection N_2) >= n-2.
But since N_1 intersection N_2 in N_1 we get
n-1 >= dim(N_1 intersection N_2) >= n-2. But since N_1 =/= N_2 obviously
dim(N_1 intersection N_2) =/= n-1 and so dim(N_1 intersection N_2) =n-2.
Is that right? Did I leave out any important step?
Thanks.
Think about this special case: V= R3, T_1(x,y,z)= (x,y,z) and T_2= (x, y, 0). What are N_1 and N_2?
 
  • #3
Sorry, I meant that both T_1 and T_2 are from V to F:
T_1:V->F
T_2:V->F
Thanks.
 

FAQ: What is the Dimension of the Intersection of Two Kernels in a Vector Space?

What is a kernel transformation?

A kernel transformation is a mathematical function that is applied to a dataset in order to change its shape or distribution. It is often used in data analysis and machine learning to make data more suitable for analysis or to improve the performance of a model.

What is the purpose of a kernel transformation?

The purpose of a kernel transformation is to change the structure or distribution of a dataset in order to make it more amenable to analysis or to improve the performance of a model. It can also help uncover hidden patterns and relationships in the data.

What are some examples of kernel transformations?

Some examples of kernel transformations include logarithmic, exponential, and polynomial transformations. Other common transformations include square root, inverse, and power transformations.

How does a kernel transformation affect the data?

A kernel transformation can change the shape, scale, and distribution of the data. It can also introduce new features or relationships between variables that were not present in the original data.

What are the benefits of using kernel transformations?

Kernel transformations can help improve the performance of machine learning models by making the data more suitable for analysis. They can also reveal hidden patterns and relationships in the data, leading to a better understanding and interpretation of the data.

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