Linear Transformation Spaces and Their Matrix Representation Spaces are Isomorphic
Theorem1
Let’s assume that two vector spaces have dimensions , respectively. And let be the ordered bases for each. Then the function defined as follows is an isomorphism.
is the matrix representation of .
Corollary
A necessary and sufficient condition for a linear transformation to be an isomorphism is that the dimensions of the domain and codomain are equal, hence,
Description
In the case of finite dimensions, every linear transformation corresponds to a matrix and vice versa. Addition and composition (matrix multiplication for matrices) are well-preserved between the two, and thus one can think of linear transformations and matrices as being essentially the same. Applying to the elements of (the left below) or multiplying the coordinate vector by the matrix representation (the right below) are essentially the same.
Proof
First, that is a linear transformation can be easily seen as follows.
Now, to show that is both injective and surjective, let us say . And let a matrix for any given be provided. Then there exists a linear transformation that satisfies the following uniquely.
This implies , in other words, . Hence, is uniquely determined for all , therefore is a bijection and an isomorphism.
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Stephen H. Friedberg, Linear Algebra (4th Edition, 2002), p103-104 ↩︎