# 11: Basis and Dimension

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In chapter 10, the notions of a linearly independent set of vectors in a vector space $$V$$, and of a set of vectors that span $$V$$ were established: Any set of vectors that span $$V$$ can be reduced to some minimal collection of linearly independent vectors; such a set is called a \emph{basis} of the subspace $$V$$.

Definitions

Let $$V$$ be a vector space.

• Then a set $$S$$ is a $$\textit{basis}$$ for $$V$$ if $$S$$ is linearly independent and $$V = span S$$.
• If $$S$$ is a basis of $$V$$ and $$S$$ has only finitely many elements, then we say that $$V$$ is $$\textit{finite-dimensional}$$.
• The number of vectors in $$S$$ is the $$\textit{dimension}$$ of $$V$$.

Suppose $$V$$ is a $$\textit{finite-dimensional}$$ vector space, and $$S$$ and $$T$$ are two different bases for $$V$$. One might worry that $$S$$ and $$T$$ have a different number of vectors; then we would have to talk about the dimension of $$V$$ in terms of the basis $$S$$ or in terms of the basis $$T$$. Luckily this isn't what happens. Later in this chapter, we will show that $$S$$ and $$T$$ must have the same number of vectors. This means that the dimension of a vector space is basis-independent. In fact, dimension is a very important characteristic of a vector space.

Example $$\PageIndex{1}$$:

$$P_{n}(t)$$ (polynomials in $$t$$ of degree $$n$$ or less) has a basis $$\{1,t,\ldots , t^{n} \}$$, since every vector in this space is a sum

$a^{0}\,1+a^{1}\,t+\cdots +a^{n}\,t^{n}, \qquad a^{i}\in \Re\, ,$

so $$P_{n}(t)=span \{1,t,\ldots , t^{n} \}$$. This set of vectors is linearly independent: If the polynomial $$p(t)=c^{0}1+c^{1}t+\cdots +c^{n}t^{n}=0$$, then $$c^{0}=c^{1}=\cdots =c^{n}=0$$, so $$p(t)$$ is the zero polynomial. Thus $$P_{n}(t)$$ is finite dimensional, and $$\dim P_{n}(t)=n+1$$.

Theorem

Let $$S=\{v_{1}, \ldots, v_{n} \}$$ be a basis for a vector space $$V$$. Then every vector $$w \in V$$ can be written $$\textit{uniquely}$$ as a linear combination of vectors in the basis $$S$$:

$w=c^{1}v_{1}+\cdots + c^{n}v_{n}.$

Proof

Since $$S$$ is a basis for $$V$$, then $$span S=V$$, and so there exist constants $$c^{i}$$ such that $$w=c^{1}v_{1}+\cdots + c^{n}v_{n}$$.

Suppose there exists a second set of constants $$d^{i}$$ such that
$$w=d^{1}v_{1}+\cdots + d^{n}v_{n}\, .$$ Then:
\begin{eqnarray*}
0_{V}&=&w-w\\
&=&c^{1}v_{1}+\cdots + c^{n}v_{n}-d^{1}v_{1}-\cdots - d^{n}v_{n} \\
&=&(c^{1}-d^{1})v_{1}+\cdots + (c^{n}-d^{n})v_{n}. \\
\end{eqnarray*}

If it occurs exactly once that $$c^{i}\neq d^{i}$$, then the equation reduces to $$0=(c^{i}-d^{i})v_{i}$$, which is a contradiction since the vectors $$v_{i}$$ are assumed to be non-zero.

If we have more than one $$i$$ for which $$c^{i}\neq d^{i}$$, we can use this last equation to write one of the vectors in $$S$$ as a linear combination of other vectors in $$S$$, which contradicts the assumption that $$S$$ is linearly independent. Then for every $$i$$, $$c^{i}=d^{i}$$.

###### Remark

This theorem is the one that makes bases so useful--they allow us to convert abstract vectors into column vectors. By ordering the set $$S$$ we obtain $$B=(v_{1},\ldots,v_{n})$$ and can write

$w=(v_{1},\ldots,v_{n}) \begin{pmatrix}c^{1}\\ \vdots\\ c^{n}\end{pmatrix}=\begin{pmatrix}c^{1}\\ \vdots\\ c^{n}\end{pmatrix}_{B}\, .$

Remember that in general it makes no sense to drop the subscript $$B$$ on the column vector on the right--most vector spaces are not made from columns of numbers!

Next, we would like to establish a method for determining whether a collection of vectors forms a basis for $$\Re^{n}$$. But first, we need to show that any two bases for a finite-dimensional vector space has the same number of vectors.

###### Lemma

If $$S=\{v_{1}, \ldots, v_{n} \}$$ is a basis for a vector space $$V$$ and $$T=\{w_{1}, \ldots, w_{m} \}$$ is a linearly independent set of vectors in $$V$$, then $$m\leq n$$.

The idea of the proof is to start with the set $$S$$ and replace vectors in $$S$$ one at a time with vectors from $$T$$, such that after each replacement we still have a basis for $$V$$.

Proof

Since $$S$$ spans $$V$$, then the set $$\{w_{1}, v_{1}, \ldots, v_{n} \}$$ is linearly dependent. Then we can write $$w_{1}$$ as a linear combination of the $$v_{i}$$; using that equation, we can express one of the $$v_{i}$$ in terms of $$w_{1}$$ and the remaining $$v_{j}$$ with $$j\neq i$$. Then we can discard one of the $$v_{i}$$ from this set to obtain a linearly independent set that still spans $$V$$. Now we need to prove that $$S_{1}$$ is a basis; we must show that $$S_{1}$$ is linearly independent and that $$S_{1}$$ spans $$V$$.

The set $$S_{1}=\{w_{1}, v_{1}, \ldots, v_{i-1}, v_{i+1},\ldots, v_{n} \}$$ is linearly independent: By the previous theorem, there was a unique way to express $$w_{1}$$ in terms of the set $$S$$. Now, to obtain a contradiction, suppose there is some $$k$$ and constants $$c^{i}$$ such that

$v_{k} = c^{0}w_{1}+c^{1}v_{1}+\cdots + c^{i-1}v_{i-1} + c^{i+1}v_{i+1} + \cdots + c^{n}v_{n}.$

Then replacing $$w_{1}$$ with its expression in terms of the collection $$S$$ gives a way to express the vector $$v_{k}$$ as a linear combination of the vectors in $$S$$, which contradicts the linear independence of $$S$$. On the other hand, we cannot express $$w_{1}$$ as a linear combination of the vectors in $$\{v_{j} | j\neq i\}$$, since the expression of $$w_{1}$$ in terms of $$S$$ was unique, and had a non-zero coefficient for the vector $$v_{i}$$. Then no vector in $$S_{1}$$ can be expressed as a combination of other vectors in $$S_{1}$$, which demonstrates that $$S_{1}$$ is linearly independent.

The set $$S_{1}$$ spans $$V$$: For any $$u\in V$$, we can express $$u$$ as a linear combination of vectors in $$S$$. But we can express $$v_{i}$$ as a linear combination of vectors in the collection $$S_{1}$$; rewriting $$v_{i}$$ as such allows us to express $$u$$ as a linear combination of the vectors in $$S_{1}$$. Thus $$S_{1}$$ is a basis of $$V$$ with $$n$$ vectors.

We can now iterate this process, replacing one of the $$v_{i}$$ in $$S_{1}$$ with $$w_{2}$$, and so on. If $$m\leq n$$, this process ends with the set $$S_{m}=\{w_{1},\ldots, w_{m}$$, $$v_{i_1},\ldots,v_{i_{n-m}} \}$$, which is fine.

Otherwise, we have $$m>n$$, and the set $$S_{n}=\{w_{1},\ldots, w_{n} \}$$ is a basis for $$V$$. But we still have some vector $$w_{n+1}$$ in $$T$$ that is not in $$S_{n}$$. Since $$S_{n}$$ is a basis, we can write $$w_{n+1}$$ as a combination of the vectors in $$S_{n}$$, which contradicts the linear independence of the set $$T$$. Then it must be the case that $$m\leq n$$, as desired.

$$\square$$

Corollary
For a finite-dimensional vector space $$V$$, any two bases for $$V$$ have the same number of vectors.

Proof
Let $$S$$ and $$T$$ be two bases for $$V$$. Then both are linearly independent sets that span $$V$$. Suppose $$S$$ has $$n$$ vectors and $$T$$ has $$m$$ vectors. Then by the previous lemma, we have that $$m\leq n$$. But (exchanging the roles of $$S$$ and $$T$$ in application of the lemma) we also see that $$n\leq m$$. Then $$m=n$$, as desired.

###### Contributor

Thumbnail: A linear combination of one basis set of vectors (purple) obtains new vectors (red). If they are linearly independent, these form a new basis set. The linear combinations relating the first set to the other extend to a linear transformation, called the change of basis. (CC0; Maschen via Wikipedia)

This page titled 11: Basis and Dimension is shared under a not declared license and was authored, remixed, and/or curated by David Cherney, Tom Denton, & Andrew Waldron.