Kernel and Image
The kernel (null space) and image (range) of a linear transformation are the two fundamental subspaces associated with it. They measure how much information the map "destroys" and "covers."
Definitions
Let be a linear transformation. The kernel (or null space) of is
The image (or range) of is
The rank of is .
The nullity of is .
For linear:
- is a subspace of .
- is a subspace of .
For (1): , so . If and , then , so .
For (2): . If and , then .
Examples
For , with .
Row reducing: .
So . Nullity , rank .
, .
(the constant polynomials).
So and , giving .
, .
(the -axis), nullity .
, rank .
, .
, the trace-zero matrices. This has dimension (nullity ).
, rank .
Check: .
(rotation by ).
(only the origin is fixed), nullity .
(rotation is surjective), rank .
, for all .
, nullity .
, rank .
, .
, nullity .
, rank .
, .
(since implies ), nullity .
(since ), rank .
For any , is the solution set of . The column space is spanned by the columns of .
, .
is the set of continuous functions with zero integral over . For instance, all odd functions belong to . This kernel is infinite-dimensional.
, .
, i.e., polynomials with root at . These are of the form with , so nullity .
, .
, which is 1-dimensional.
(surjective: for any , take ).
Injectivity and surjectivity
A linear map is injective (one-to-one) if and only if .
() If is injective and , then .
() If and , then , so , i.e., .
is surjective (onto) if and only if .
The precise relationship between kernel and image is given by the Rank-Nullity Theorem: . A linear map that is both injective and surjective is an isomorphism.