# Cokernel or Range

A basis β for a vector space V is a linearly independent subset of V that generates
or span V. If β is a basis for V, we also say that elements of β form a basis for V.
This means that every vector from V is a finite linear combination of elements from the basis.

Recall that a set of vectors β is said to generate or span a vector space V if every element from V can be represented as a linear combination of vectors from β.

Example: The span of the empty set $$\varnothing$$ consists of a unique element 0. Therefore, $$\varnothing$$ is linearly independent and it is a basis for the trivial vector space consisting of the unique element---zero. Its dimension is zero.

Example: In $$\mathbb{R}^n ,$$ the vectors $$e_1 [1,0,0,\ldots , 0] , \quad e_2 =[0,1,0,\ldots , 0], \quad \ldots , e_n =[0,0,\ldots , 0,1]$$ form a basis for n-dimensional real space, and it is called the standard basis. Its dimension is n.

Example: Let us consider the set of all real $$m \times n$$ matrices, and let $${\bf M}_{i,j}$$ denote the matrix whose only nonzero entry is a 1 in the i-th row and j-th column. Then the set $${\bf M}_{i,j} \ : \ 1 \le i \le m , \ 1 \le j \le n$$ is a basis for the set of all such real matrices. Its dimension is mn.

Example: The set of monomials $$\left\{ 1, x, x^2 , \ldots , x^n \right\}$$ form a basis in the set of all polynomials of degree up to n. It has dimension n+1. ■

Example: The infinite set of monomials $$\left\{ 1, x, x^2 , \ldots , x^n , \ldots \right\}$$ form a basis in the set of all polynomials. ■

Theorem: Let V be a vector space and $$\beta = \left\{ {\bf u}_1 , {\bf u}_2 , \ldots , {\bf u}_n \right\}$$ be a subset of V. Then β is a basis for V if and only if each vector v in V can be uniquely decomposed into a linear combination of vectors in β, that is, can be uniquely expressed in the form

${\bf v} = \alpha_1 {\bf u}_1 + \alpha_2 {\bf u}_2 + \cdots + \alpha_n {\bf u}_n$
for unique scalars $$\alpha_1 , \alpha_2 , \ldots , \alpha_n .$$