Linear Transformation R4 to R4: KerT + ImT = R4

In this case, V = W = R4, and dim(V) = dim(W) = 4, so the statement becomes dim(Ker(T)) + dim(Im(t)) = 4.In summary, the statement "KerT + ImT = R4" is true for a linear transformation T defined on R4 to R4, but it may not be true for linear transformations with different domain and codomain. This is due to the Rank-Nullity Theorem, which states that for T:V --> W, dim(Ker(T)) + dim(Im(T)) = dim(V) = dim(W).
  • #1
Dank2
213
4

Homework Statement


Let T be a Linear Transformation defined on R4 ---> R4
Is that true that the following is always true ?
KerT + ImT = R4

Homework Equations

The Attempt at a Solution


Since every vector in R4 must be either in KerT or the ImT, so the addition of those subspace contains R. and ofc every vector in ImT or KerT is in R4.
 
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  • #2
Dank2 said:

Homework Statement


Let T be a Linear Transformation defined on R4 ---> R4
Is that true that the following is always true ?
KerT + ImT = R4

Homework Equations

The Attempt at a Solution


Since every vector in R4 must be either in KerT or the ImT, so the addition of those subspace contains R. and ofc every vector in ImT or KerT is in R4.
Looks OK to me other than you omiitted the 4 in R4, which I'm sure was inadvertent.

Given that T is a transformation from a space to itself, your statement is true. If, however, the domain and codomain weren't the same, then the statement would not be true, as Ker(T) and Im(T) would be subspaces of different dimension.

For example, if T is defined as ##T : \mathbb{R}^3 \to \mathbb{R}^2##, with ##T\begin{pmatrix} x \\ y \\ z \end{pmatrix} = \begin{pmatrix} x \\ y \end{pmatrix}## then Ker(T) consists of all vectors of the form ##\begin{pmatrix} 0 \\ 0 \\ z \end{pmatrix}##, a subspace of ##\mathbb{R}^3## while Im(T) consists of all vectors of the form ##\begin{pmatrix} x \\ y \end{pmatrix}##, a subspace of ##\mathbb{R}^2##.

The idea behind this question seems to be the Rank-Nullity Theorem. For a linear transformation T:V --> W, it's usually stated as dim(Ker(T)) + dim(Im(t)) = dim(V).
 

1. What is a linear transformation from R4 to R4?

A linear transformation from R4 to R4 is a function that takes a four-dimensional vector as an input and produces another four-dimensional vector as an output. In other words, it is a mathematical operation that maps one four-dimensional space to another four-dimensional space.

2. What does KerT + ImT = R4 mean?

KerT + ImT = R4 is a way to represent the range of a linear transformation from R4 to R4. KerT is the kernel, or null space, of the transformation, which consists of all vectors that are mapped to the zero vector. ImT is the image, or span, of the transformation, which consists of all possible outputs. R4 represents the entire four-dimensional space. Together, KerT + ImT = R4 means that the range of the transformation covers the entire four-dimensional space.

3. How do you determine if a linear transformation from R4 to R4 is injective?

A linear transformation from R4 to R4 is injective if and only if the kernel, or null space, of the transformation is equal to the zero vector. This means that the only vector that is mapped to the zero vector is the zero vector itself. In other words, every input vector has a unique output.

4. What is an example of a linear transformation from R4 to R4?

One example of a linear transformation from R4 to R4 is the transformation that rotates a four-dimensional vector by a certain angle around a specified axis. This transformation takes in a four-dimensional vector as an input and produces a different four-dimensional vector as an output, while preserving the dimensionality of the space.

5. How is a linear transformation from R4 to R4 used in real life?

Linear transformations from R4 to R4 are used in various fields such as engineering, computer graphics, and physics. In engineering, these transformations are used to model and analyze physical systems, such as the motion of a particle in four-dimensional space. In computer graphics, they are used to manipulate and transform objects in 3D or 4D space. In physics, they are used to represent physical phenomena that occur in four-dimensional space, such as the behavior of particles in quantum mechanics.

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