Kernel subsets of transformations

In summary: So you want to show that the S is a linear transformation, and that it satisfies T1 = S∘T2.In summary, the conversation discusses the relationship between linear transformations T1 and T2 and their respective kernel spaces. It is shown that there exists a linear transformation S such that T1 is equal to the composition of S and T2 if and only if the kernel of T2 is a subset of the kernel of T1. The key steps in the proof involve showing that S is linear and that it satisfies the desired equality.
  • #1
dustbin
240
5

Homework Statement



Let [itex] T_1,T_2:ℝ^n\rightarrowℝ^n [/itex] be linear transformations. Show that [itex] \exists S:ℝ^n\rightarrowℝ^n [/itex] s.t. [itex] T_1=S\circ T_2 \Longleftrightarrow kerT_2\subset kerT_1 [/itex].

The Attempt at a Solution



[itex] (\Longrightarrow) [/itex] Let [itex] S:ℝ^n\rightarrowℝ^n [/itex] be a linear transformation s.t. [itex] T_1 = S\circ T_2 [/itex] and let [itex]\vec{v}\in kerT_2 [/itex]. Then [itex] S(T_2(\vec{v})) = S(\vec{0}) = \vec{0} [/itex] by linearity. Then [itex]T_1(\vec{v}) = \vec{0}[/itex]. Thus [itex] \vec{v}\in kerT_1 \quad \forall\vec{v}\in kerT_2 [/itex]. Therefore [itex]kerT_2 \subset kerT_1 [/itex].

[itex] (\Longleftarrow) [/itex] Suppose that [itex] kerT_2\subset kerT_1[/itex] and choose [itex]S:ℝ^n\rightarrowℝ^n[/itex] s.t. [itex]S[/itex] is linear and [itex]T_1 = S\circ T_2 [/itex]. Then for [itex]\vec{v}\in kerT_2,\quad T_1(\vec{v}) = S(T_2(\vec{v}) = S(\vec{0}) = \vec{0}. [/itex] Thus there exists such a transformation.
 
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  • #2
dustbin said:

Homework Statement



Let [itex] T_1,T_2:ℝ^n\rightarrowℝ^n [/itex] be linear transformations. Show that [itex] \exists S:ℝ^n\rightarrowℝ^n [/itex] s.t. [itex] T_1=S\circ T_2 \Longleftrightarrow kerT_2\subset kerT_1 [/itex].

The Attempt at a Solution



[itex] (\Longrightarrow) [/itex] Let [itex] S:ℝ^n\rightarrowℝ^n [/itex] be a linear transformation s.t. [itex] T_1 = S\circ T_2 [/itex] and let [itex]\vec{v}\in kerT_2 [/itex]. Then [itex] S(T_2(\vec{v})) = S(\vec{0}) = \vec{0} [/itex] by linearity. Then [itex]T_1(\vec{v}) = \vec{0}[/itex]. Thus [itex] \vec{v}\in kerT_1 \quad \forall\vec{v}\in kerT_2 [/itex]. Therefore [itex]kerT_2 \subset kerT_1 [/itex].

[itex] (\Longleftarrow) [/itex] Suppose that [itex] kerT_2\subset kerT_1[/itex] and choose [itex]S:ℝ^n\rightarrowℝ^n[/itex] s.t. [itex]S[/itex] is linear and [itex]T_1 = S\circ T_2 [/itex]. Then for [itex]\vec{v}\in kerT_2,\quad T_1(\vec{v}) = S(T_2(\vec{v}) = S(\vec{0}) = \vec{0}. [/itex] Thus there exists such a transformation.

The first part seems ok. For the second, the problem is to show that such an S exists given ker(T2) is contained in ker(T1). Not to assume it exists.
 
Last edited:

1. What are kernel subsets of transformations?

Kernel subsets of transformations refer to a subset of a transformation that maps a vector to the zero vector. This subset is also known as the null space or kernel of the transformation.

2. How are kernel subsets of transformations useful?

Kernel subsets of transformations are useful in many areas of mathematics and science, such as linear algebra, differential equations, and computer graphics. They help us understand the properties and behavior of a transformation, and can be used to solve systems of equations and analyze data.

3. How do you find the kernel subset of a transformation?

To find the kernel subset of a transformation, you can set the transformation equal to the zero vector and solve for the variables. The solutions to this equation will be the vectors that make up the kernel subset.

4. Can the kernel subset of a transformation be empty?

Yes, it is possible for the kernel subset of a transformation to be empty. This occurs when there are no solutions to the equation that defines the kernel subset, meaning that the transformation does not have any vectors that map to the zero vector.

5. How is the dimension of the kernel subset related to the dimension of the transformation's domain and range?

The dimension of the kernel subset is related to the dimension of the transformation's domain and range through the rank-nullity theorem. This theorem states that the dimension of the kernel subset plus the dimension of the range will always equal the dimension of the domain.

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