Linear Algebra: Kernel & Range of Linear Transformation

In summary, we are interested in looking at the kernel and range (image) of a linear transformation on a linear algebra course because they can provide useful information about the linearity of the map. The kernel can be used to determine if the map is injective, while the quotient space $V/\mbox{ker}(f)$ is isomorphic to the image, making it easier to work with. There are many other results related to this topic, but these two are commonly used and easy to remember.
  • #1
matqkks
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Why are we interested in looking at the kernel and range (image) of a linear transformation on a linear algebra course?
 
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  • #2
They can be useful. Suppose you have a linear map [tex]f: V \to W[/tex]. If you want to know if this linear map is injective (i.e one-to-one map) then you can take a look at the kernel: $$\ker( f)=\{0\} \Leftrightarrow \ f \ \mbox{is injective}$$

There's also the following result.
$$V/\mbox{ker}( f) \cong \mbox{Im}( f)$$
which can be very useful because it's easier to work with the image in stead of the quotientspace $V/\mbox{ker}( f)$

These are offcourse a lot of other results but these two are the first I could remember immediately.
 

FAQ: Linear Algebra: Kernel & Range of Linear Transformation

What is the kernel of a linear transformation?

The kernel of a linear transformation is the set of all vectors in the domain that get mapped to the zero vector in the co-domain. In other words, it is the set of all inputs that produce an output of zero.

How is the kernel related to the range of a linear transformation?

The kernel and the range of a linear transformation are complementary subspaces. This means that any vector in the range is perpendicular to any vector in the kernel, and vice versa. In addition, the dimension of the kernel and the dimension of the range add up to the dimension of the domain.

How can the kernel be used to determine if a linear transformation is injective?

A linear transformation is injective if and only if the kernel contains only the zero vector. This means that the transformation maps each input to a unique output, as no two inputs can produce the same output.

Can the kernel of a linear transformation be empty?

Yes, the kernel can be empty if the only vector that gets mapped to the zero vector is the zero vector itself. This would mean that the transformation is injective and has a trivial kernel.

How can the kernel be used to solve systems of linear equations?

The kernel can be used to find the solutions to homogeneous systems of linear equations. This is because the solutions to such systems correspond to the vectors in the kernel of the coefficient matrix. By finding a basis for the kernel, we can find all the solutions to the system.

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