A linear transformation is any type of function that operates over a vector space and preserves it. In other words, a transformed vector space is still a vector space and still follows the axioms of a vector space.
See also Geometric Transformation
Definition
Let and be two vector spaces fixed over the field, . A linear transformation, , from to is notated as:
And it must preserve vector addition and scalar multiplication. If and :
More tersely, we can say a linear transformation is one that preserves linear combinations:
Furthermore, the zero vector of is always mapped to the zero vector of :
Standard Matrix Representation
Matrix Representation of a Linear Transformation
Composition
Since linear transformations are functions, the concept of function composition also applies:
Composition of Linear Transformations (Functions) ^theorem
Let be vector spaces fixed under the same field, . Then, let and be two linear transformations:
Composition of Linear Transformations (Matrices) ^theorem
Let and be two linear transformations in the real set. Then, their standard matrices are given by and
In other words, the transformation matrix of a composition of transformation is given by matrix multiplying the two individual transformation matrices.
Kernel & Image
The concepts of matrix rank and nullity can be extended to linear transformations as well, albeit in a different sense:
Injective & Surjective
Because a linear transformation is a function, it can share some similar properties.
Injective Linear Transformations
A linear transformation is injective if it’s kernel only has one element: the zero vector. Let be a linear transformation. Then:
Surjective Linear Transformations
A linear transformation is surjective if it’s image is equal to it’s codomain. Let be a linear transformation. Then:
This can give rise to an Invertible Linear Transformation.