Ewu Math 231 Matrix Representations

Given a linear transformation T with domain U (basis B) and codomain V (basis C) we can construct a

After defining an eigenvalue-eigenvector relationship for a linear transformation T on a vector space V, we dive into a

Having learned that the linear system LS(A, b) can be represented as a vector equation Ax = b with

Given any finite dimensional vector space V, We discuss a new function with domain V and codomain C^n. It turns out that this ...

We define the left null space of a

After defining

Similar to the section on Vector Operations, we define the vector space of m x n

We link the property of invertible to the property of nonsingular. This link allows us to add to our list of Nonsingular

We define the column space of a

We discuss a characteristic of a square

We leverage the fact that equation operations only change the coefficients of a system of linear equations. We use a

We discuss what makes a function from U to V linear. After some terminology, we see that linear transformations between vector ...

We define the dimension of a vector space as the number of vectors in a basis. After seeing that more vectors in a set than needed ...