A Linear transformation is a Transformation where
- where
- Domain of T is (where we start)
- Codomain or target of T is
- The vector is the image of under T
- The set of all possible images is called the range
- image range codomain
- When the domain and codomain are both , you can represent them as a Cartesian Graph in , as in a mapping of
- If y is the codomain and x is the domain, the range is the range, the domain is all the images of f(x)
- Addition Rule
- T(u + v) = T(u) + T(v)
- Multiplication Rule
- T(c) = cT(v)
- Prove a transformation is linear by proving the addition and multiplication rules.
- “Principle of Superposition”
- If we know , …, then we know every T(v)
1-1
Injective
If there is at most one location in the Codomain for every location in the Domain
Link to original
- 1-1 every column of T is a pivot column
- 1-1 iff standard matrix has pivot in every column
Onto
Surjective
If there is a location in the Codomain for every location in the Domain
Link to original
- TLDR: Onto every row of T is a pivot row
- onto iff the standard matrix has a pivot in every row
- The matrix A has columns which span .
- The matrix A has all pivotal rows.
Bijective
Bijective
If there is exactly one location in the Codomain for every location in the Domain for the Transformation
Link to original
- Needs square matrix
Matrix Multiplication is a Linear Transformation
Compute
Calculate so that
Give a Give a that is not in the range of Give a that is not in the span of the columns of (Same question for all)
Range of is a bunch of images of the following form:
For to not be in the range of , it cannot be in the above form, e.g. it can be