Matrices aren’t just grids of numbers—they’re powerful tools for performing linear transformations. Stretching, rotating, reflecting, and projecting: matrix operations reshape space and reveal structure in data, geometry, and beyond. 🔄
Linear Transformation
A transformation (function or mapping) T : \mathbb{R}^n \rightarrow \mathbb{R}^m denoted T from \mathbb{R}^n to \mathbb{R}^m is a rule that assigns to each vector \mathbf{x} \in \mathbb{R}^n a vector T(\mathbf{x}) \in \mathbb{R}^m.
Matrices serve as linear transformations
T(\mathbf{x}) = A\mathbf{x}
The set \mathbb{R}^n is called the domain of T, and the set \mathbb{R}^m is called the codomain of T.
Assume a matrix A with dimensions m x n, we can compute the dot product or matrix multiplication by
To perform the matrix multiplication A\mathbf{x}, the number of columns in A must match the number of entries in the vector \mathbf{x}.
That is, if A is an m \times n matrix and \mathbf{x} is an n \times 1 column vector, the multiplication is valid and the result will be an m \times 1 column vector.
If the dimensions do not align, the dot product is undefined.